Google App Rating Prediction Project.ipynb

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  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cab40db5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "30772c17",
   "metadata": {},
   "outputs": [],
   "source": [
    "#1. Load the data file using pandas. \n",
    "\n",
    "data = pd.read_csv(\"googleplaystore.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "93239423",
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>App</th>\n",
       "      <th>Category</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Size</th>\n",
       "      <th>Installs</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Content Rating</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Last Updated</th>\n",
       "      <th>Current Ver</th>\n",
       "      <th>Android Ver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Photo Editor &amp; Candy Camera &amp; Grid &amp; ScrapBook</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.1</td>\n",
       "      <td>159</td>\n",
       "      <td>19M</td>\n",
       "      <td>10,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>January 7, 2018</td>\n",
       "      <td>1.0.0</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Coloring book moana</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>3.9</td>\n",
       "      <td>967</td>\n",
       "      <td>14M</td>\n",
       "      <td>500,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design;Pretend Play</td>\n",
       "      <td>January 15, 2018</td>\n",
       "      <td>2.0.0</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U Launcher Lite – FREE Live Cool Themes, Hide ...</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.7</td>\n",
       "      <td>87510</td>\n",
       "      <td>8.7M</td>\n",
       "      <td>5,000,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>August 1, 2018</td>\n",
       "      <td>1.2.4</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sketch - Draw &amp; Paint</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.5</td>\n",
       "      <td>215644</td>\n",
       "      <td>25M</td>\n",
       "      <td>50,000,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Teen</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>June 8, 2018</td>\n",
       "      <td>Varies with device</td>\n",
       "      <td>4.2 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pixel Draw - Number Art Coloring Book</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.3</td>\n",
       "      <td>967</td>\n",
       "      <td>2.8M</td>\n",
       "      <td>100,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design;Creativity</td>\n",
       "      <td>June 20, 2018</td>\n",
       "      <td>1.1</td>\n",
       "      <td>4.4 and up</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 App        Category  Rating  \\\n",
       "0     Photo Editor & Candy Camera & Grid & ScrapBook  ART_AND_DESIGN     4.1   \n",
       "1                                Coloring book moana  ART_AND_DESIGN     3.9   \n",
       "2  U Launcher Lite – FREE Live Cool Themes, Hide ...  ART_AND_DESIGN     4.7   \n",
       "3                              Sketch - Draw & Paint  ART_AND_DESIGN     4.5   \n",
       "4              Pixel Draw - Number Art Coloring Book  ART_AND_DESIGN     4.3   \n",
       "\n",
       "  Reviews  Size     Installs  Type Price Content Rating  \\\n",
       "0     159   19M      10,000+  Free     0       Everyone   \n",
       "1     967   14M     500,000+  Free     0       Everyone   \n",
       "2   87510  8.7M   5,000,000+  Free     0       Everyone   \n",
       "3  215644   25M  50,000,000+  Free     0           Teen   \n",
       "4     967  2.8M     100,000+  Free     0       Everyone   \n",
       "\n",
       "                      Genres      Last Updated         Current Ver  \\\n",
       "0               Art & Design   January 7, 2018               1.0.0   \n",
       "1  Art & Design;Pretend Play  January 15, 2018               2.0.0   \n",
       "2               Art & Design    August 1, 2018               1.2.4   \n",
       "3               Art & Design      June 8, 2018  Varies with device   \n",
       "4    Art & Design;Creativity     June 20, 2018                 1.1   \n",
       "\n",
       "    Android Ver  \n",
       "0  4.0.3 and up  \n",
       "1  4.0.3 and up  \n",
       "2  4.0.3 and up  \n",
       "3    4.2 and up  \n",
       "4    4.4 and up  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "24269544",
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   "outputs": [
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       "      <th></th>\n",
       "      <th>App</th>\n",
       "      <th>Category</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Size</th>\n",
       "      <th>Installs</th>\n",
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       "      <th>Android Ver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10836</th>\n",
       "      <td>Sya9a Maroc - FR</td>\n",
       "      <td>FAMILY</td>\n",
       "      <td>4.5</td>\n",
       "      <td>38</td>\n",
       "      <td>53M</td>\n",
       "      <td>5,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Education</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>10837</th>\n",
       "      <td>Fr. Mike Schmitz Audio Teachings</td>\n",
       "      <td>FAMILY</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.6M</td>\n",
       "      <td>100+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Education</td>\n",
       "      <td>July 6, 2018</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.1 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10838</th>\n",
       "      <td>Parkinson Exercices FR</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>9.5M</td>\n",
       "      <td>1,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Medical</td>\n",
       "      <td>January 20, 2017</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.2 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10839</th>\n",
       "      <td>The SCP Foundation DB fr nn5n</td>\n",
       "      <td>BOOKS_AND_REFERENCE</td>\n",
       "      <td>4.5</td>\n",
       "      <td>114</td>\n",
       "      <td>Varies with device</td>\n",
       "      <td>1,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Mature 17+</td>\n",
       "      <td>Books &amp; Reference</td>\n",
       "      <td>January 19, 2015</td>\n",
       "      <td>Varies with device</td>\n",
       "      <td>Varies with device</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10840</th>\n",
       "      <td>iHoroscope - 2018 Daily Horoscope &amp; Astrology</td>\n",
       "      <td>LIFESTYLE</td>\n",
       "      <td>4.5</td>\n",
       "      <td>398307</td>\n",
       "      <td>19M</td>\n",
       "      <td>10,000,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Lifestyle</td>\n",
       "      <td>July 25, 2018</td>\n",
       "      <td>Varies with device</td>\n",
       "      <td>Varies with device</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 App             Category  \\\n",
       "10836                               Sya9a Maroc - FR               FAMILY   \n",
       "10837               Fr. Mike Schmitz Audio Teachings               FAMILY   \n",
       "10838                         Parkinson Exercices FR              MEDICAL   \n",
       "10839                  The SCP Foundation DB fr nn5n  BOOKS_AND_REFERENCE   \n",
       "10840  iHoroscope - 2018 Daily Horoscope & Astrology            LIFESTYLE   \n",
       "\n",
       "       Rating Reviews                Size     Installs  Type Price  \\\n",
       "10836     4.5      38                 53M       5,000+  Free     0   \n",
       "10837     5.0       4                3.6M         100+  Free     0   \n",
       "10838     NaN       3                9.5M       1,000+  Free     0   \n",
       "10839     4.5     114  Varies with device       1,000+  Free     0   \n",
       "10840     4.5  398307                 19M  10,000,000+  Free     0   \n",
       "\n",
       "      Content Rating             Genres      Last Updated         Current Ver  \\\n",
       "10836       Everyone          Education     July 25, 2017                1.48   \n",
       "10837       Everyone          Education      July 6, 2018                 1.0   \n",
       "10838       Everyone            Medical  January 20, 2017                 1.0   \n",
       "10839     Mature 17+  Books & Reference  January 19, 2015  Varies with device   \n",
       "10840       Everyone          Lifestyle     July 25, 2018  Varies with device   \n",
       "\n",
       "              Android Ver  \n",
       "10836          4.1 and up  \n",
       "10837          4.1 and up  \n",
       "10838          2.2 and up  \n",
       "10839  Varies with device  \n",
       "10840  Varies with device  "
      ]
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10841 entries, 0 to 10840\n",
      "Data columns (total 13 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   App             10841 non-null  object \n",
      " 1   Category        10841 non-null  object \n",
      " 2   Rating          9367 non-null   float64\n",
      " 3   Reviews         10841 non-null  object \n",
      " 4   Size            10841 non-null  object \n",
      " 5   Installs        10841 non-null  object \n",
      " 6   Type            10840 non-null  object \n",
      " 7   Price           10841 non-null  object \n",
      " 8   Content Rating  10840 non-null  object \n",
      " 9   Genres          10841 non-null  object \n",
      " 10  Last Updated    10841 non-null  object \n",
      " 11  Current Ver     10833 non-null  object \n",
      " 12  Android Ver     10838 non-null  object \n",
      "dtypes: float64(1), object(12)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
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  {
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   "execution_count": 24,
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       "    <tr>\n",
       "      <th>10839</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10840</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10841 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         App  Category  Rating  Reviews   Size  Installs   Type  Price  \\\n",
       "0      False     False   False    False  False     False  False  False   \n",
       "1      False     False   False    False  False     False  False  False   \n",
       "2      False     False   False    False  False     False  False  False   \n",
       "3      False     False   False    False  False     False  False  False   \n",
       "4      False     False   False    False  False     False  False  False   \n",
       "...      ...       ...     ...      ...    ...       ...    ...    ...   \n",
       "10836  False     False   False    False  False     False  False  False   \n",
       "10837  False     False   False    False  False     False  False  False   \n",
       "10838  False     False    True    False  False     False  False  False   \n",
       "10839  False     False   False    False  False     False  False  False   \n",
       "10840  False     False   False    False  False     False  False  False   \n",
       "\n",
       "       Content Rating  Genres  Last Updated  Current Ver  Android Ver  \n",
       "0               False   False         False        False        False  \n",
       "1               False   False         False        False        False  \n",
       "2               False   False         False        False        False  \n",
       "3               False   False         False        False        False  \n",
       "4               False   False         False        False        False  \n",
       "...               ...     ...           ...          ...          ...  \n",
       "10836           False   False         False        False        False  \n",
       "10837           False   False         False        False        False  \n",
       "10838           False   False         False        False        False  \n",
       "10839           False   False         False        False        False  \n",
       "10840           False   False         False        False        False  \n",
       "\n",
       "[10841 rows x 13 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2. Check for null values in the data. Get the number of null values for each column.\n",
    "\n",
    "data.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2440350b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "App               False\n",
       "Category          False\n",
       "Rating             True\n",
       "Reviews           False\n",
       "Size              False\n",
       "Installs          False\n",
       "Type               True\n",
       "Price             False\n",
       "Content Rating     True\n",
       "Genres            False\n",
       "Last Updated      False\n",
       "Current Ver        True\n",
       "Android Ver        True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#3. Drop records with nulls in any of the columns. \n",
    "\n",
    "data.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4b798353",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a54892f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "App               False\n",
       "Category          False\n",
       "Rating            False\n",
       "Reviews           False\n",
       "Size              False\n",
       "Installs          False\n",
       "Type              False\n",
       "Price             False\n",
       "Content Rating    False\n",
       "Genres            False\n",
       "Last Updated      False\n",
       "Current Ver       False\n",
       "Android Ver       False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "215cc4de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9360, 13)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0fb389b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#4. Variables seem to have incorrect type and inconsistent formatting. You need to fix them: \n",
    "#4.1 Size column has sizes in Kb as well as Mb. To analyze, you’ll need to convert these to numeric.\n",
    "#4.1.1 Extract the numeric value from the column\n",
    "\n",
    "data[\"Size\"] = [ float(i.split('M')[0]) if 'M' in i else float(0) for i in data[\"Size\"]  ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7e0c59fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>App</th>\n",
       "      <th>Category</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
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       "      <th>Android Ver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Photo Editor &amp; Candy Camera &amp; Grid &amp; ScrapBook</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.1</td>\n",
       "      <td>159</td>\n",
       "      <td>19.0</td>\n",
       "      <td>10,000+</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1.0.0</td>\n",
       "      <td>4.0.3 and up</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Coloring book moana</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>3.9</td>\n",
       "      <td>967</td>\n",
       "      <td>14.0</td>\n",
       "      <td>500,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design;Pretend Play</td>\n",
       "      <td>January 15, 2018</td>\n",
       "      <td>2.0.0</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U Launcher Lite – FREE Live Cool Themes, Hide ...</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.7</td>\n",
       "      <td>87510</td>\n",
       "      <td>8.7</td>\n",
       "      <td>5,000,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>August 1, 2018</td>\n",
       "      <td>1.2.4</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sketch - Draw &amp; Paint</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.5</td>\n",
       "      <td>215644</td>\n",
       "      <td>25.0</td>\n",
       "      <td>50,000,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Teen</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>June 8, 2018</td>\n",
       "      <td>Varies with device</td>\n",
       "      <td>4.2 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pixel Draw - Number Art Coloring Book</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.3</td>\n",
       "      <td>967</td>\n",
       "      <td>2.8</td>\n",
       "      <td>100,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design;Creativity</td>\n",
       "      <td>June 20, 2018</td>\n",
       "      <td>1.1</td>\n",
       "      <td>4.4 and up</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 App        Category  Rating  \\\n",
       "0     Photo Editor & Candy Camera & Grid & ScrapBook  ART_AND_DESIGN     4.1   \n",
       "1                                Coloring book moana  ART_AND_DESIGN     3.9   \n",
       "2  U Launcher Lite – FREE Live Cool Themes, Hide ...  ART_AND_DESIGN     4.7   \n",
       "3                              Sketch - Draw & Paint  ART_AND_DESIGN     4.5   \n",
       "4              Pixel Draw - Number Art Coloring Book  ART_AND_DESIGN     4.3   \n",
       "\n",
       "  Reviews  Size     Installs  Type Price Content Rating  \\\n",
       "0     159  19.0      10,000+  Free     0       Everyone   \n",
       "1     967  14.0     500,000+  Free     0       Everyone   \n",
       "2   87510   8.7   5,000,000+  Free     0       Everyone   \n",
       "3  215644  25.0  50,000,000+  Free     0           Teen   \n",
       "4     967   2.8     100,000+  Free     0       Everyone   \n",
       "\n",
       "                      Genres      Last Updated         Current Ver  \\\n",
       "0               Art & Design   January 7, 2018               1.0.0   \n",
       "1  Art & Design;Pretend Play  January 15, 2018               2.0.0   \n",
       "2               Art & Design    August 1, 2018               1.2.4   \n",
       "3               Art & Design      June 8, 2018  Varies with device   \n",
       "4    Art & Design;Creativity     June 20, 2018                 1.1   \n",
       "\n",
       "    Android Ver  \n",
       "0  4.0.3 and up  \n",
       "1  4.0.3 and up  \n",
       "2  4.0.3 and up  \n",
       "3    4.2 and up  \n",
       "4    4.4 and up  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "95e59a89",
   "metadata": {},
   "outputs": [],
   "source": [
    "#4.1.2 Multiply the value by 1,000, if size is mentioned in Mb\n",
    "data['Size'] = 1000 * data['Size']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "91cbf284",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>App</th>\n",
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       "      <th>0</th>\n",
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       "      <td>14000.0</td>\n",
       "      <td>500,000+</td>\n",
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       "      <td>2.0.0</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U Launcher Lite – FREE Live Cool Themes, Hide ...</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.7</td>\n",
       "      <td>87510</td>\n",
       "      <td>8700.0</td>\n",
       "      <td>5,000,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>August 1, 2018</td>\n",
       "      <td>1.2.4</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sketch - Draw &amp; Paint</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.5</td>\n",
       "      <td>215644</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>50,000,000+</td>\n",
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       "      <td>0</td>\n",
       "      <td>Teen</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>June 8, 2018</td>\n",
       "      <td>Varies with device</td>\n",
       "      <td>4.2 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pixel Draw - Number Art Coloring Book</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.3</td>\n",
       "      <td>967</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>100,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design;Creativity</td>\n",
       "      <td>June 20, 2018</td>\n",
       "      <td>1.1</td>\n",
       "      <td>4.4 and up</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 App        Category  Rating  \\\n",
       "0     Photo Editor & Candy Camera & Grid & ScrapBook  ART_AND_DESIGN     4.1   \n",
       "1                                Coloring book moana  ART_AND_DESIGN     3.9   \n",
       "2  U Launcher Lite – FREE Live Cool Themes, Hide ...  ART_AND_DESIGN     4.7   \n",
       "3                              Sketch - Draw & Paint  ART_AND_DESIGN     4.5   \n",
       "4              Pixel Draw - Number Art Coloring Book  ART_AND_DESIGN     4.3   \n",
       "\n",
       "  Reviews     Size     Installs  Type Price Content Rating  \\\n",
       "0     159  19000.0      10,000+  Free     0       Everyone   \n",
       "1     967  14000.0     500,000+  Free     0       Everyone   \n",
       "2   87510   8700.0   5,000,000+  Free     0       Everyone   \n",
       "3  215644  25000.0  50,000,000+  Free     0           Teen   \n",
       "4     967   2800.0     100,000+  Free     0       Everyone   \n",
       "\n",
       "                      Genres      Last Updated         Current Ver  \\\n",
       "0               Art & Design   January 7, 2018               1.0.0   \n",
       "1  Art & Design;Pretend Play  January 15, 2018               2.0.0   \n",
       "2               Art & Design    August 1, 2018               1.2.4   \n",
       "3               Art & Design      June 8, 2018  Varies with device   \n",
       "4    Art & Design;Creativity     June 20, 2018                 1.1   \n",
       "\n",
       "    Android Ver  \n",
       "0  4.0.3 and up  \n",
       "1  4.0.3 and up  \n",
       "2  4.0.3 and up  \n",
       "3    4.2 and up  \n",
       "4    4.4 and up  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "dcde0c83",
   "metadata": {},
   "outputs": [
    {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10834</th>\n",
       "      <td>FR Calculator</td>\n",
       "      <td>FAMILY</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>500+</td>\n",
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       "    <tr>\n",
       "      <th>10836</th>\n",
       "      <td>Sya9a Maroc - FR</td>\n",
       "      <td>FAMILY</td>\n",
       "      <td>4.5</td>\n",
       "      <td>38</td>\n",
       "      <td>53000.0</td>\n",
       "      <td>5,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Education</td>\n",
       "      <td>July 25, 2017</td>\n",
       "      <td>1.48</td>\n",
       "      <td>4.1 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10837</th>\n",
       "      <td>Fr. Mike Schmitz Audio Teachings</td>\n",
       "      <td>FAMILY</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>100+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Education</td>\n",
       "      <td>July 6, 2018</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.1 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10839</th>\n",
       "      <td>The SCP Foundation DB fr nn5n</td>\n",
       "      <td>BOOKS_AND_REFERENCE</td>\n",
       "      <td>4.5</td>\n",
       "      <td>114</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Mature 17+</td>\n",
       "      <td>Books &amp; Reference</td>\n",
       "      <td>January 19, 2015</td>\n",
       "      <td>Varies with device</td>\n",
       "      <td>Varies with device</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10840</th>\n",
       "      <td>iHoroscope - 2018 Daily Horoscope &amp; Astrology</td>\n",
       "      <td>LIFESTYLE</td>\n",
       "      <td>4.5</td>\n",
       "      <td>398307</td>\n",
       "      <td>19000.0</td>\n",
       "      <td>10,000,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Lifestyle</td>\n",
       "      <td>July 25, 2018</td>\n",
       "      <td>Varies with device</td>\n",
       "      <td>Varies with device</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 App             Category  \\\n",
       "10834                                  FR Calculator               FAMILY   \n",
       "10836                               Sya9a Maroc - FR               FAMILY   \n",
       "10837               Fr. Mike Schmitz Audio Teachings               FAMILY   \n",
       "10839                  The SCP Foundation DB fr nn5n  BOOKS_AND_REFERENCE   \n",
       "10840  iHoroscope - 2018 Daily Horoscope & Astrology            LIFESTYLE   \n",
       "\n",
       "       Rating Reviews     Size     Installs  Type Price Content Rating  \\\n",
       "10834     4.0       7   2600.0         500+  Free     0       Everyone   \n",
       "10836     4.5      38  53000.0       5,000+  Free     0       Everyone   \n",
       "10837     5.0       4   3600.0         100+  Free     0       Everyone   \n",
       "10839     4.5     114      0.0       1,000+  Free     0     Mature 17+   \n",
       "10840     4.5  398307  19000.0  10,000,000+  Free     0       Everyone   \n",
       "\n",
       "                  Genres      Last Updated         Current Ver  \\\n",
       "10834          Education     June 18, 2017               1.0.0   \n",
       "10836          Education     July 25, 2017                1.48   \n",
       "10837          Education      July 6, 2018                 1.0   \n",
       "10839  Books & Reference  January 19, 2015  Varies with device   \n",
       "10840          Lifestyle     July 25, 2018  Varies with device   \n",
       "\n",
       "              Android Ver  \n",
       "10834          4.1 and up  \n",
       "10836          4.1 and up  \n",
       "10837          4.1 and up  \n",
       "10839  Varies with device  \n",
       "10840  Varies with device  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2430883d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 9360 entries, 0 to 10840\n",
      "Data columns (total 13 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   App             9360 non-null   object \n",
      " 1   Category        9360 non-null   object \n",
      " 2   Rating          9360 non-null   float64\n",
      " 3   Reviews         9360 non-null   object \n",
      " 4   Size            9360 non-null   float64\n",
      " 5   Installs        9360 non-null   object \n",
      " 6   Type            9360 non-null   object \n",
      " 7   Price           9360 non-null   object \n",
      " 8   Content Rating  9360 non-null   object \n",
      " 9   Genres          9360 non-null   object \n",
      " 10  Last Updated    9360 non-null   object \n",
      " 11  Current Ver     9360 non-null   object \n",
      " 12  Android Ver     9360 non-null   object \n",
      "dtypes: float64(2), object(11)\n",
      "memory usage: 1023.8+ KB\n"
     ]
    }
   ],
   "source": [
    "#4.2 Reviews is a numeric field that is loaded as a string field. Convert it to numeric (int/float).\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4b0f8f13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           159\n",
       "1           967\n",
       "2         87510\n",
       "3        215644\n",
       "4           967\n",
       "          ...  \n",
       "10834         7\n",
       "10836        38\n",
       "10837         4\n",
       "10839       114\n",
       "10840    398307\n",
       "Name: Reviews, Length: 9360, dtype: object"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Reviews\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a7dc02d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"Reviews\"] = data[\"Reviews\"].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b44fe84b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 9360 entries, 0 to 10840\n",
      "Data columns (total 13 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   App             9360 non-null   object \n",
      " 1   Category        9360 non-null   object \n",
      " 2   Rating          9360 non-null   float64\n",
      " 3   Reviews         9360 non-null   int32  \n",
      " 4   Size            9360 non-null   float64\n",
      " 5   Installs        9360 non-null   object \n",
      " 6   Type            9360 non-null   object \n",
      " 7   Price           9360 non-null   object \n",
      " 8   Content Rating  9360 non-null   object \n",
      " 9   Genres          9360 non-null   object \n",
      " 10  Last Updated    9360 non-null   object \n",
      " 11  Current Ver     9360 non-null   object \n",
      " 12  Android Ver     9360 non-null   object \n",
      "dtypes: float64(2), int32(1), object(10)\n",
      "memory usage: 987.2+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "25367ab1",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"Reviews\"] = data[\"Reviews\"].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d15cce97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 9360 entries, 0 to 10840\n",
      "Data columns (total 13 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   App             9360 non-null   object \n",
      " 1   Category        9360 non-null   object \n",
      " 2   Rating          9360 non-null   float64\n",
      " 3   Reviews         9360 non-null   float64\n",
      " 4   Size            9360 non-null   float64\n",
      " 5   Installs        9360 non-null   object \n",
      " 6   Type            9360 non-null   object \n",
      " 7   Price           9360 non-null   object \n",
      " 8   Content Rating  9360 non-null   object \n",
      " 9   Genres          9360 non-null   object \n",
      " 10  Last Updated    9360 non-null   object \n",
      " 11  Current Ver     9360 non-null   object \n",
      " 12  Android Ver     9360 non-null   object \n",
      "dtypes: float64(3), object(10)\n",
      "memory usage: 1023.8+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "bdf919ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "#4.3 Installs field is currently stored as string and has values like 1,000,000+. \n",
    "#4.3.1 Treat 1,000,000+ as 1,000,000\n",
    "\n",
    "data[\"Installs\"] = [ float(i.replace('+','').replace(',', '')) if '+' in i or ',' in i else float(0) for i in data[\"Installs\"] ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "121a11b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>App</th>\n",
       "      <th>Category</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Size</th>\n",
       "      <th>Installs</th>\n",
       "      <th>Type</th>\n",
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       "      <th>Current Ver</th>\n",
       "      <th>Android Ver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Photo Editor &amp; Candy Camera &amp; Grid &amp; ScrapBook</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.1</td>\n",
       "      <td>159.0</td>\n",
       "      <td>19000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>January 7, 2018</td>\n",
       "      <td>1.0.0</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Coloring book moana</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>3.9</td>\n",
       "      <td>967.0</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design;Pretend Play</td>\n",
       "      <td>January 15, 2018</td>\n",
       "      <td>2.0.0</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U Launcher Lite – FREE Live Cool Themes, Hide ...</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.7</td>\n",
       "      <td>87510.0</td>\n",
       "      <td>8700.0</td>\n",
       "      <td>5000000.0</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>August 1, 2018</td>\n",
       "      <td>1.2.4</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sketch - Draw &amp; Paint</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.5</td>\n",
       "      <td>215644.0</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>50000000.0</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Teen</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>June 8, 2018</td>\n",
       "      <td>Varies with device</td>\n",
       "      <td>4.2 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pixel Draw - Number Art Coloring Book</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.3</td>\n",
       "      <td>967.0</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design;Creativity</td>\n",
       "      <td>June 20, 2018</td>\n",
       "      <td>1.1</td>\n",
       "      <td>4.4 and up</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 App        Category  Rating  \\\n",
       "0     Photo Editor & Candy Camera & Grid & ScrapBook  ART_AND_DESIGN     4.1   \n",
       "1                                Coloring book moana  ART_AND_DESIGN     3.9   \n",
       "2  U Launcher Lite – FREE Live Cool Themes, Hide ...  ART_AND_DESIGN     4.7   \n",
       "3                              Sketch - Draw & Paint  ART_AND_DESIGN     4.5   \n",
       "4              Pixel Draw - Number Art Coloring Book  ART_AND_DESIGN     4.3   \n",
       "\n",
       "    Reviews     Size    Installs  Type Price Content Rating  \\\n",
       "0     159.0  19000.0     10000.0  Free     0       Everyone   \n",
       "1     967.0  14000.0    500000.0  Free     0       Everyone   \n",
       "2   87510.0   8700.0   5000000.0  Free     0       Everyone   \n",
       "3  215644.0  25000.0  50000000.0  Free     0           Teen   \n",
       "4     967.0   2800.0    100000.0  Free     0       Everyone   \n",
       "\n",
       "                      Genres      Last Updated         Current Ver  \\\n",
       "0               Art & Design   January 7, 2018               1.0.0   \n",
       "1  Art & Design;Pretend Play  January 15, 2018               2.0.0   \n",
       "2               Art & Design    August 1, 2018               1.2.4   \n",
       "3               Art & Design      June 8, 2018  Varies with device   \n",
       "4    Art & Design;Creativity     June 20, 2018                 1.1   \n",
       "\n",
       "    Android Ver  \n",
       "0  4.0.3 and up  \n",
       "1  4.0.3 and up  \n",
       "2  4.0.3 and up  \n",
       "3    4.2 and up  \n",
       "4    4.4 and up  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "56bec02f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 9360 entries, 0 to 10840\n",
      "Data columns (total 13 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   App             9360 non-null   object \n",
      " 1   Category        9360 non-null   object \n",
      " 2   Rating          9360 non-null   float64\n",
      " 3   Reviews         9360 non-null   float64\n",
      " 4   Size            9360 non-null   float64\n",
      " 5   Installs        9360 non-null   float64\n",
      " 6   Type            9360 non-null   object \n",
      " 7   Price           9360 non-null   object \n",
      " 8   Content Rating  9360 non-null   object \n",
      " 9   Genres          9360 non-null   object \n",
      " 10  Last Updated    9360 non-null   object \n",
      " 11  Current Ver     9360 non-null   object \n",
      " 12  Android Ver     9360 non-null   object \n",
      "dtypes: float64(4), object(9)\n",
      "memory usage: 1023.8+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b78a0b96",
   "metadata": {},
   "outputs": [],
   "source": [
    "#4.3.2 Converted it to integer\n",
    "data[\"Installs\"] = data[\"Installs\"].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "7e0f34f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>App</th>\n",
       "      <th>Category</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Photo Editor &amp; Candy Camera &amp; Grid &amp; ScrapBook</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.1</td>\n",
       "      <td>159.0</td>\n",
       "      <td>19000.0</td>\n",
       "      <td>10000</td>\n",
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       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Coloring book moana</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>3.9</td>\n",
       "      <td>967.0</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>500000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design;Pretend Play</td>\n",
       "      <td>January 15, 2018</td>\n",
       "      <td>2.0.0</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U Launcher Lite – FREE Live Cool Themes, Hide ...</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.7</td>\n",
       "      <td>87510.0</td>\n",
       "      <td>8700.0</td>\n",
       "      <td>5000000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>August 1, 2018</td>\n",
       "      <td>1.2.4</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sketch - Draw &amp; Paint</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.5</td>\n",
       "      <td>215644.0</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>50000000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Teen</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>June 8, 2018</td>\n",
       "      <td>Varies with device</td>\n",
       "      <td>4.2 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pixel Draw - Number Art Coloring Book</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.3</td>\n",
       "      <td>967.0</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>100000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design;Creativity</td>\n",
       "      <td>June 20, 2018</td>\n",
       "      <td>1.1</td>\n",
       "      <td>4.4 and up</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 App        Category  Rating  \\\n",
       "0     Photo Editor & Candy Camera & Grid & ScrapBook  ART_AND_DESIGN     4.1   \n",
       "1                                Coloring book moana  ART_AND_DESIGN     3.9   \n",
       "2  U Launcher Lite – FREE Live Cool Themes, Hide ...  ART_AND_DESIGN     4.7   \n",
       "3                              Sketch - Draw & Paint  ART_AND_DESIGN     4.5   \n",
       "4              Pixel Draw - Number Art Coloring Book  ART_AND_DESIGN     4.3   \n",
       "\n",
       "    Reviews     Size  Installs  Type Price Content Rating  \\\n",
       "0     159.0  19000.0     10000  Free     0       Everyone   \n",
       "1     967.0  14000.0    500000  Free     0       Everyone   \n",
       "2   87510.0   8700.0   5000000  Free     0       Everyone   \n",
       "3  215644.0  25000.0  50000000  Free     0           Teen   \n",
       "4     967.0   2800.0    100000  Free     0       Everyone   \n",
       "\n",
       "                      Genres      Last Updated         Current Ver  \\\n",
       "0               Art & Design   January 7, 2018               1.0.0   \n",
       "1  Art & Design;Pretend Play  January 15, 2018               2.0.0   \n",
       "2               Art & Design    August 1, 2018               1.2.4   \n",
       "3               Art & Design      June 8, 2018  Varies with device   \n",
       "4    Art & Design;Creativity     June 20, 2018                 1.1   \n",
       "\n",
       "    Android Ver  \n",
       "0  4.0.3 and up  \n",
       "1  4.0.3 and up  \n",
       "2  4.0.3 and up  \n",
       "3    4.2 and up  \n",
       "4    4.4 and up  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f3e2ea44",
   "metadata": {},
   "outputs": [],
   "source": [
    "#4.4 Price field is a string and has $ symbol. Remove ‘$’ sign, and convert it to numeric.\n",
    "\n",
    "data['Price'] = [ float(i.split('$')[1]) if '$' in i else float(0) for i in data['Price'] ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "62873164",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"Price\"] = data[\"Price\"].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "1b195c32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 9360 entries, 0 to 10840\n",
      "Data columns (total 13 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   App             9360 non-null   object \n",
      " 1   Category        9360 non-null   object \n",
      " 2   Rating          9360 non-null   float64\n",
      " 3   Reviews         9360 non-null   float64\n",
      " 4   Size            9360 non-null   float64\n",
      " 5   Installs        9360 non-null   int32  \n",
      " 6   Type            9360 non-null   object \n",
      " 7   Price           9360 non-null   int32  \n",
      " 8   Content Rating  9360 non-null   object \n",
      " 9   Genres          9360 non-null   object \n",
      " 10  Last Updated    9360 non-null   object \n",
      " 11  Current Ver     9360 non-null   object \n",
      " 12  Android Ver     9360 non-null   object \n",
      "dtypes: float64(3), int32(2), object(8)\n",
      "memory usage: 950.6+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "0b068582",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9360, 13)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#4.5 Sanity checks:\n",
    "#4.5.1 Average rating should be between 1 and 5 as only these values are allowed on the play store. Drop the rows that have a value outside this range.\n",
    "\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "06532ad6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9360, 13)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(data[(data['Reviews'] < 1) & (data['Reviews'] > 5 )].index, inplace = True)\n",
    "data.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "a6d8e3a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9360, 13)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#4.5.2 Reviews should not be more than installs as only those who installed can review the app. If there are any such records, drop them.\n",
    "\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "a208ce6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9353, 13)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(data[(data['Installs'] ) <  (data['Reviews'] )].index, inplace = True)\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "05d01a8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#4.5.3 For free apps (type = “Free”), the price should not be >0. Drop any such rows.\n",
    "\n",
    "data.drop(data[(data['Type'] =='Free') & (data['Price'] > 0 )].index, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d9f0b6fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9353, 13)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "a0ab1c5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 9353 entries, 0 to 10840\n",
      "Data columns (total 13 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   App             9353 non-null   object \n",
      " 1   Category        9353 non-null   object \n",
      " 2   Rating          9353 non-null   float64\n",
      " 3   Reviews         9353 non-null   float64\n",
      " 4   Size            9353 non-null   float64\n",
      " 5   Installs        9353 non-null   int32  \n",
      " 6   Type            9353 non-null   object \n",
      " 7   Price           9353 non-null   int32  \n",
      " 8   Content Rating  9353 non-null   object \n",
      " 9   Genres          9353 non-null   object \n",
      " 10  Last Updated    9353 non-null   object \n",
      " 11  Current Ver     9353 non-null   object \n",
      " 12  Android Ver     9353 non-null   object \n",
      "dtypes: float64(3), int32(2), object(8)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "8330746c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\new\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Price'>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#5. Performing univariate analysis: \n",
    "#5.1 Boxplot for Price. Are there any outliers? Think about the price of usual apps on Play Store.\n",
    "\n",
    "sns.set(rc={'figure.figsize':(20,8)})\n",
    "sns.boxplot(data['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "b1adacd7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\new\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Reviews'>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data['Reviews'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "ffd85566",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\new\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Rating'>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jhRdeiIaGhth3333j8ssvj2HDhtV6agBAnRNqAAAAAJLwZcIAAAAASQg1AAAAAEkINQAAAABJCDUAAAAASQg1AAAAAEn8fxOAlfwzd39nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data['Rating'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "8bef333a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\new\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Size'>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0KK6++urYvn17/PjHP44f/ehH1Z4iAECb8eu5AYC0du7cGV/+8pfjgQceiOHDh0dExJw5c2LmzJkxf/786Nq1a5VnCADQtuyoAQDS2rZtW2zatCm2bt26+2unnXZa9OrVKxYtWhS33XZbzJ07N84///x4//33IyJi69atsXLlypg3b14ceuihMWPGjFiyZEk0NDTE5z73ubjuuuuiV69e1bokAIBWCTUAQFp9+vSJSZMmxQUXXBD9+/ePY445Jo477rgYO3ZsLF26dPe4+++/PyI+2oEzYcKEOOuss2LYsGExa9as6Nq1azz22GNRU1MTd955Z8yYMSO+973vVemKAABa56NPAEB6mzdvjiVLlsSSJUti/vz5ERExadKkuOuuu2Lu3LkREdHY2Bjf/e53o2/fvjFt2rSIiDjzzDNj06ZN0bNnz4iIqK+vj379+sVDDz1UnQsBANgDO2oAgLRefvnlePXVV+OCCy6I0aNHx+jRo+OKK66IcePGxa5du5qMvfnmm2Pbtm0xc+bM3V9rbGyMKVOmRF1dXUREbNmyJXbs2NGh1wAA8HH49dwAQFqHHHJI3HPPPfHSSy/t/tratWtj8+bNsWHDht1fu+++++LVV1+Nu+66q8kPGD7hhBPi4Ycfjp07d0ZjY2Ncf/31ceedd3bkJQAAfCw++gQApPb73/8+7r777nj33XejR48e0bt375g4cWL06NEjbrzxxrj//vujrq4uPvWpT0XPnj2jsbExIiIuvfTSGDlyZEyfPj0WL14cDQ0N8dnPfjZuvPFGP0wYAEhLqAEAAABIwkefAAAAAJIQagAAAACSEGoAAAAAkhBqAAAAAJIQagAAAACSEGoAAAAAkhBqAAAAAJL4/w7XdJVwvxJdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data['Size'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "149e1d23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Size', ylabel='Count'>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Histogram for Size\n",
    "\n",
    "sns.histplot(data['Size'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "4ad46aab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Rating', ylabel='Count'>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tceaZZ8YZZ5wRERFTp06N5ubmeN/73heHDx/uuUyvqakp7rrrrrjqqqve1QwAAAAApJdXaMpkMvH5z38+yQNu3749qqqq4rrrrotdu3bFRRddFGPGjIlsNtuzT11dXbS2tsbu3buP2Z7NZqO1tbVPjzdy5LAkcw8E2WxtsUegSKx9+bL25cm6ly9rX76sffl6N2tfVdW3jxQp9M9ZX+bp6yyFfq7FOH5vjznQ5iedcvje5/XTOGbMmHjuuefi/e9//7t+wKNHj8aTTz4Z9913X7znPe+J66+/Pk466aRj3imVy+WioqIiuru733J7X+zd2x7d3bl3PXexZbO10dZ2oNhjUATWvnxZ+/Jk3cuXtS9f1r58vZu1z2Zro7Ozq09fU8ifs77O05dZCv1ci3H8qqpMr485kOYnnVL5+76ysqLXN/XkFZp27NgR06ZNi9/6rd+Kmpqanu0/+MEP+jzQb/7mb8aECRPilFNOiYiIP/qjP4rm5uYYMmRIzz5tbW1RV1cXo0aNira2tp7te/bsibq6uj4/JgAAAACFl1doev03wKVw8cUXx8033xz79++Pk08+OR577LGYMmVKfPvb347t27fH6aefHuvWrYtp06bF6NGjo6amJjZv3hznn39+rFmzJurr65PNAgAAAEA6eYWms88+O9kDnnfeefGnf/qncdVVV0VnZ2dMnDgxrrzyyvid3/mdmDVrVnR0dERDQ0NMmTIlIiKWLVsWCxYsiPb29hg3blzMmDEj2SwAAAAApJNXaPrwhz8cFRUVx3xGUjabjR/96Efv6EGnT58e06dPP2bbhAkTYu3atcftO3bs2Fi9evU7ehwAAAAA+k9eoenZZ5/t+fORI0di3bp18cILLxRsKAAAAAAGn8q+fkF1dXU0NTXFE088UYh5AAAAABik8npH06uvvtrz51wuF1u2bIn9+/cXaiYAAAAABqE+f0ZTRMTIkSNj/vz5BR0MAAAAgMGlz5/RBAAAAABvJa/Q1N3dHffcc0/86Ec/iq6urpg4cWJcd911kcnk9eUAAAAAlIG8StHXvva1ePbZZ+Oaa66J7u7u+O53vxtLliyJefPmFXo+AACAQWf4iKFRU33il1vZbG1ERHQc6Yr9+w4VeiyAgssrND322GPxve99L6qqqiIi4qKLLopLL71UaAIAAHgLNdWZmLOipdd9qqoy0dnZFRERS2c39MdYAAVXmc9OuVyuJzJFRFRXVx9zGwAAAADyCk1jx46N22+/PX71q1/Fjh074vbbb4+zzz670LMBAAAAMIjkFZoWLVoU+/fvjyuuuCL++I//OF555ZX4yle+UujZAAAAABhEeg1NR44ciZtvvjk2bdoUixcvjo0bN8a5554bQ4YMiWHDhvXXjAAAAAAMAr2Gprvuuiva29vjQx/6UM+2W2+9Nfbv3x/f+MY3Cj4cAAAAAINHr6HpP//zP+NrX/tajBw5smfbqaeeGkuWLIl///d/L/hwAAAAAAwevYamqqqqOOmkk47bPmzYsKiuri7YUAAAAAAMPr2GpsrKymhvbz9ue3t7e3R1dRVsKAAAAAAGn15D0yWXXBILFiyIgwcP9mw7ePBgLFiwICZNmlTw4QAAAAAYPHoNTddcc03U1tbGxIkT49Of/nRMnz49Jk6cGMOHD48bbrihv2YEAAAAYBDI9HZnZWVl3HrrrXHdddfFM888E5WVlXHuuedGXV1df80HAAAAwCDRa2h63ejRo2P06NGFngUAAACAQazXS+cAAAAAIF9CEwAAAABJCE0AAAAAJCE0AQAAAJCE0AQAAABAEkITAAAAAEkITQAAAAAkITQBAAAAkITQBAAAAEASQhMAAAAASQhNAAAAACQhNAEAAACQRKbYAwAAAAAD0/ARQ6OmOv900HGkK/bvO1TAiRjohCYAAADgLdVUZ2LOipa89186u6GA0zAYuHQOAAAAgCSEJgAAAACSEJoAAAAASEJoAgAAACAJoQkAAACAJIQmAAAAAJIQmgAAAABIQmgCAAAAIAmhCQAAAIAkhCYAAAAAkhCaAAAAAEhCaAIAAAAgCaEJAAAAgCSEJgAAAACSEJoAAAAASEJoAgAAACAJoQkAAACAJIQmAAAAAJIQmgAAAABIQmgCAAAAIAmhCQAAAIAkhCYAAAAAkhCaAAAAAEhCaAIAAAAgCaEJAAAAgCSEJgAAAACSEJoAAAAASEJoAgAAACAJoQkAAACAJIQmAAAAAJIQmgAAAABIQmgCAAAAIImihqY777wz5s6dGxERGzdujKlTp8akSZNi+fLlPfts3bo1mpqaYvLkyTF//vzo6uoq1rgAAAAA9KJooWnTpk3x0EMPRUTE4cOHY968ebFy5cpYv359bNmyJVpaWiIiYs6cObFw4cJ4+OGHI5fLxapVq4o1MgAAAAC9KEpoevXVV2P58uVx3XXXRUTE008/HWeeeWacccYZkclkYurUqdHc3Bw7d+6Mw4cPx/jx4yMioqmpKZqbm4sxMgAAAAAnkCnGgy5cuDBuuumm2LVrV0RE7N69O7LZbM/9dXV10draetz2bDYbra2tfXqskSOHpRl6AMhma4s9AkVi7cuXtS9P1r18WfvyZe1LU1XViV9uvXGfvv4c5HP8Nyr0z1lf5hloz7UYx+/tMQfD/CmPX07K4XvT76HpgQceiNNOOy0mTJgQDz74YEREdHd3R0VFRc8+uVwuKioq3nZ7X+zd2x7d3bk0wxdRNlsbbW0Hij0GRWDty5e1L0/WvXxZ+/Jl7UtTNlsbnZ29f75sVVXmmH368nOQz/HfrJA/Z32dZyA912Ic/81rn/r4JzKQjl9OSuXv+8rKil7f1NPvoWn9+vXR1tYWl112Wezbty8OHjwYO3fujCFDhvTs09bWFnV1dTFq1Khoa2vr2b5nz56oq6vr75EBAAAAyEO/h6Z77723588PPvhg/Pd//3fccsstMWnSpNi+fXucfvrpsW7dupg2bVqMHj06ampqYvPmzXH++efHmjVror6+vr9HBgAAACAPRfmMpjerqamJxYsXx6xZs6KjoyMaGhpiypQpERGxbNmyWLBgQbS3t8e4ceNixowZRZ4WAAAAgLdS1NDU1NQUTU1NERExYcKEWLt27XH7jB07NlavXt3fowEAACVs+IihUVOd/8uhjiNdsX/foQJOBFAaBsQ7mgAAAPpTTXUm5qxoyXv/pbMbCjgNQOmoLPYAAAAAAJQGoQkAAACAJIQmAAAAAJIQmgAAAABIQmgCAAAAIAmhCQAAAIAkhCYAAAAAkhCaAAAAAEhCaAIAAAAgCaEJAAAAgCSEJgAAAACSEJoAAAAASEJoAgAAACAJoQkAAACAJIQmAAAAAJIQmgAAAABIQmgCAAAAIAmhCQAAAIAkhCYAAAAAkhCaAAAAAEhCaAIAAAAgCaEJAAAAgCSEJgAAAACSEJoAAAAASEJoAgAAACAJoQkAAACAJIQmAAAAAJIQmgAAAABIQmgCAAAAIIlMsQcAAAB4s+EjhkZNdf4vVzqOdMX+fYcKOBEA+RCaAACAAaemOhNzVrTkvf/S2Q0FnAaAfLl0DgAAAIAkhCYAAAAAkhCaAAAAAEhCaAIAAAAgCaEJAAAAgCSEJgAAAACSEJoAAAAASEJoAgAAACAJoQkAAACAJIQmAAAAAJIQmgAAAABIIlPsAQAAAAAGo+EjhkZNdX5ppbOru8DTDAxCEwAAAMA7UFOdiTkrWvLad+nshgJPMzC4dA4AAACAJIQmAAAAAJIQmgAAAABIQmgCAAAAIAmhCQAAAIAkhCYAAAAAkhCaAAAAAEhCaAIAAAAgCaEJAAAAgCQyxR4AAAAYfIaPGBo11fm/nOg40hX79x0q4EQADARCEwAA0Gc11ZmYs6Il7/2Xzm4o4DQADBRCEwAAAFAU3h1ZeoQmAAAAoCi8O7L0+DBwAAAAAJIQmgAAAABIQmgCAAAAIAmhCQAAAIAkhCYAAAAAkhCaAAAAAEhCaAIAAAAgiaKEprvvvjsaGxujsbExlixZEhERGzdujKlTp8akSZNi+fLlPftu3bo1mpqaYvLkyTF//vzo6uoqxsgAAAAAnEC/h6aNGzfG448/Hg899FB8//vfj2eeeSbWrVsX8+bNi5UrV8b69etjy5Yt0dLSEhERc+bMiYULF8bDDz8cuVwuVq1a1d8jAwAAAJCHfg9N2Ww25s6dG9XV1VFVVRVnnXVWbNu2Lc4888w444wzIpPJxNSpU6O5uTl27twZhw8fjvHjx0dERFNTUzQ3N/f3yAAAAADkIdPfDzhmzJieP2/bti1++MMfxp/8yZ9ENpvt2V5XVxetra2xe/fuY7Zns9lobW3t0+ONHDns3Q89QGSztcUegSKx9uXL2pcn616+rH35GqxrX1XVt5cTfX2e5XD8N+4z0Obvq77MM9CeazGO39tjDob5B9PxC62QP/uDUb+Hptc9//zz8YUvfCG+9KUvxZAhQ2Lbtm099+VyuaioqIju7u6oqKg4bntf7N3bHt3duVRjF002WxttbQeKPQZFYO3Ll7UvT9a9fFn78jVY1z6brY3Ozr59fmpfnmc5HL+qKnPMPgNp/r7q6zwD6bkW4/hvXvvUxz+Rcjp+oRXyZ3+gqqys6PVNPUX5MPDNmzfH5z73ufjiF78Yl19+eYwaNSra2tp67m9ra4u6urrjtu/Zsyfq6uqKMTIAAAAAJ9DvoWnXrl1xww03xLJly6KxsTEiIs4777x44YUXYvv27XH06NFYt25d1NfXx+jRo6OmpiY2b94cERFr1qyJ+vr6/h4ZAAAAgDz0+6Vz99xzT3R0dMTixYt7tl1xxRWxePHimDVrVnR0dERDQ0NMmTIlIiKWLVsWCxYsiPb29hg3blzMmDGjv0cGAAAAIA/9HpoWLFgQCxYseMv71q5de9y2sWPHxurVqws9FgAAAADvUlE+owkAAACA0iM0AQAAAJCE0AQAAABAEkITAAAAAEn0+4eBAwAAhTd8xNCoqc7/n/sdR7pi/75DBZwIgHIgNAEAQAmqqc7EnBUtee+/dHZDAacBoFy4dA4AAACAJIQmAAAAAJJw6RwAABTJ8BFDIyIim6094b4+QwmAwUBoAgCAIqmpzsS8lU9EZ2fXCff1GUoADAYunQMAAAAgCaEJAAAAgCSEJgAAAACSEJoAAAAASEJoAgAAACAJoQkAAACAJIQmAAAAAJIQmgAAAABIQmgCAAAAIAmhCQAAAIAkhCYAAAAAksgUewAAABioho8YGjXV+f+TueNIV+zfd6iAEwHAwCY0AQDA26ipzsScFS157790dkMBpwGAgc+lcwAAAAAkITQBAAAAkITQBAAAAEASQhMAAAAASQhNAAAAACQhNAEAAACQhNAEAAAAQBJCEwAAAABJZIo9AAAAvG74iKFRU53/P1E7jnTF/n2HCjgRANAXQhMAAANGTXUm5qxoyXv/pbMbCjgNANBXLp0DAAAAIAmhCQAAAIAkhCYAAAAAkhCaAAAAAEjCh4EDAAAAJclvM+1/QhMAQD/q7R+82Wztcdv8gxcA3jm/zbT/CU0AAP3o7f7BW1WVic7OruO2+wcvADCY+IwmAAAAAJIQmgAAAABIQmgCAAAAIAmf0QQAUEL8dh0AoJiEJgCAEuK36wAAxeTSOQAAAACS8I4mAADy5tI8AKA3QhMAAHlzaR4A0BuXzgEAAACQhNAEAAAAQBJCEwAAAABJCE0AAAAAJOHDwAEA3sBvVQMAeOeEJgCAN/Bb1QAA3jmhCQAYVLzjCABg4BKaAIBBxTuOAAAGLh8GDgAAAEASQhMAAAAASQhNAAAAACQhNAEAAACQhA8DB4AyVOjf3OY3wwEAlCehCQD6QV/CSzZbW/DwUujf3OY3wwEAlCehCYBBYbC/Ayff8FJVlYnOzi7hBQCAQUloGiQ6u7ojm63Ne3+XIAClxjtwAABg4BOaBomqTKUXQDAA+NyZt+d7AwAACE0AfVDod728Odac6J2MAynWeEcQAAAwKELTD37wg/jmN78ZXV1dcc0118TVV19d7JGA/2+gvYtloM3TV2+MNa9/Vk9v+hJrBvv3BgAAGPgGfGhqbW2N5cuXx4MPPhjV1dVxxRVXxIUXXhjve9/7ij0aEAPvXSwDbZ6BxPcGAAAotAEfmjZu3Bgf/vCH473vfW9EREyePDmam5vjz//8z/P6+srKigJO179+o7amT/uX0nMvpmG1J/X5XSDtBw4nPf4bL58qxPHfqK/Hjxh4P5uFnqe/jp+pykRX55Ckxy+V700pH/+N6+5cKc6xi3X83s75wTD/YDp+XxV6nvfW1uT19/07OfZgX6tSP/6bz/uBNn9f9WWegfZc+/v4J/p33kCf3/Hf+fFL4XX6iZ5DRS6Xy/XTLO/It771rTh48GDcdNNNERHxwAMPxNNPPx233nprkScDAAAA4I0qiz3AiXR3d0dFxf/Vslwud8xtAAAAAAaGAR+aRo0aFW1tbT2329raoq6urogTAQAAAPBWBnxo+shHPhKbNm2Kl19+OQ4dOhSPPPJI1NfXF3ssAAAAAN5kwH8Y+Kmnnho33XRTzJgxIzo7O2P69Olx7rnnFnssAAAAAN5kwH8YOAAAAACDw4C/dA4AAACAwUFoAgAAACAJoQkAAACAJIQmAAAAAJIQmgAAAABIQmgaYNrb2+OSSy6JF1988bj7tm7dGk1NTTF58uSYP39+dHV1FWFCCqW3tb/77rvj4osvjssuuywuu+yyuP/++4swIYVw9913R2NjYzQ2NsaSJUuOu995X7pOtPbO+9K1YsWK+OQnPxmNjY1x7733Hne/8750nWjtnfel7c4774y5c+cet905X/rebu2d86Xrs5/9bDQ2Nvas7VNPPXXM/SV/3ucYMP73f/83d8kll+TGjRuX27Fjx3H3NzY25n7605/mcrlc7stf/nLu/vvv7+cJKZQTrf0XvvCF3E9+8pMiTEYhPfHEE7nPfOYzuY6OjtyRI0dyM2bMyD3yyCPH7OO8L035rL3zvjT9+Mc/zl1xxRW5zs7O3KFDh3IXX3xx7he/+MUx+zjvS1M+a++8L10bN27MXXjhhbmbb775uPuc86Wtt7V3zpem7u7u3Ec/+tFcZ2fn2+5T6ue9dzQNIKtWrYpFixZFXV3dcfft3LkzDh8+HOPHj4+IiKampmhubu7nCSmU3tY+ImLLli3xrW99K6ZOnRpf/epXo6Ojo58npBCy2WzMnTs3qquro6qqKs4666x46aWXeu533peuE619hPO+VP3BH/xB/OM//mNkMpnYu3dvHD16NN7znvf03O+8L10nWvsI532pevXVV2P58uVx3XXXHXefc7609bb2Ec75UvXLX/4yIiKuvfbauPTSS+Of/umfjrm/HM57oWkAue222+KCCy54y/t2794d2Wy253Y2m43W1tb+Go0C623tX3vttfjABz4Qc+bMiYceeij2798fK1eu7OcJKYQxY8b0/Adm27Zt8cMf/jAaGhp67nfel64Trb3zvrRVVVXFXXfdFY2NjTFhwoQ49dRTe+5z3pe23tbeeV+6Fi5cGDfddFMMHz78uPuc86Wtt7V3zpeu/fv3x4QJE+Jv//Zv4+///u/jX/7lX+KJJ57oub8cznuhaZDo7u6OioqKntu5XO6Y25Suk08+Of7u7/4uzjrrrMhkMnHttddGS0tLsccioeeffz6uvfba+NKXvhS//du/3bPdeV/63m7tnfel78Ybb4xNmzbFrl27YtWqVT3bnfel7+3W3nlfmh544IE47bTTYsKECW95v3O+dJ1o7Z3zpeuDH/xgLFmyJGpra+OUU06J6dOnH7O25XDeC02DxKhRo6Ktra3n9p49e972MitKy0svvRSrV6/uuZ3L5SKTyRRxIlLavHlzfO5zn4svfvGLcfnllx9zn/O+tPW29s770vWLX/witm7dGhERQ4cOjUmTJsVzzz3Xc7/zvnSdaO2d96Vp/fr18cQTT8Rll10Wd911V2zYsCFuv/32nvud86XrRGvvnC9dTz75ZGzatKnn9pvXthzOe6FpkBg9enTU1NTE5s2bIyJizZo1UV9fX+Sp6A8nnXRSLF26NHbs2BG5XC7uv//++PjHP17ssUhg165dccMNN8SyZcuisbHxuPud96XrRGvvvC9dL774YixYsCCOHDkSR44ciUcffTTOP//8nvud96XrRGvvvC9N9957b6xbty7WrFkTN954Y3zsYx+LefPm9dzvnC9dJ1p753zpOnDgQCxZsiQ6Ojqivb09HnrooWPWthzOe6FpgPuzP/uz+NnPfhYREcuWLYs77rgjpkyZEgcPHowZM2YUeToK6fW1P+WUU+KrX/1qXH/99TFlypTI5XLx+c9/vtjjkcA999wTHR0dsXjx4p5fffqd73zHeV8GTrT2zvvS1dDQEBdddFF86lOfimnTpsUHP/jBaGxsdN6XgROtvfO+vDjny5dzvvRdfPHF0dDQ0PP3/et/55fTeV+Ry+VyxR4CAAAAgMHPO5oAAAAASEJoAgAAACAJoQkAAACAJIQmAAAAAJIQmgAAAABIIlPsAQAASsH73//+OPvss6OysjIqKiri0KFDMWzYsPirv/qrOOecc3r92gceeCCOHDkSV199dXznO9+JAwcOxMyZM/tpcgCAdIQmAIBE/uEf/iFOOeWUntv33HNP/PVf/3V897vf7fXrNm/eHGPGjImIiCuvvLKgMwIAFJLQBABQAF1dXbFr164YMWJERETs2bMnFi5cGHv37o22trYYPXp0fP3rX4+f/OQnsWHDhnjiiSfipJNOipdffjleeeWVWLhwYXzsYx+Lyy+/PDZt2hS7du2Kyy67LP7iL/4iIiK+/e1vx+rVq+Pkk0+OCy64IB599NHYsGFDEZ8xAIDQBACQzDXXXBMREa+88krU1NTExRdfHHfccUdERPzrv/5rjB8/PmbOnBm5XC5mzpwZa9asiWuvvTYeffTRGDNmTFx99dXxjW9845hjHjx4MP75n/85Wltb4+Mf/3hMmzYttm3bFg8++GCsXr06amtrY/78+f3+XAEA3orQBACQyOuXzj3zzDMxc+bMuPDCC2PkyJER8esI9eSTT8a9994b27Zti+effz7OO++8Ex7zD//wDyMi4tRTT42RI0fGvn37oqWlJaZMmRLDhw+PiIirr746/uu//qtwTwwAIE9CEwBAYuPGjYsvf/nLMXfu3PjABz4Qp59+eixdujSefvrpmDZtWlx44YXR1dUVuVzuhMeqqanp+XNFRUXkcrnIZDLHfO2QIUMK8jwAAPqqstgDAACUoksuuSTOPffcnkvnHn/88bjmmmviU5/6VIwcOTI2btwYR48ejYhfh6Kurq68j93Q0BCPPPJIHDhwICIiVq9enf4JAAC8A97RBABQIF/5ylfi0ksvjcceeyxuuOGGWLJkSaxYsSKqqqriQx/6UPzqV7+KiIj6+vpYvHhx3sedMGFCfPrTn47PfOYzcdJJJ8WYMWNi6NChhXoaAAB5q8jl855tAAAGjJ/97Gfx05/+NGbMmBEREffee2889dRT8fWvf724gwEAZU9oAgAYZNrb22PevHnxy1/+MioqKuK0006LW2+9NU499dRijwYAlDmhCQAAAIAkfBg4AAAAAEkITQAAAAAkITQBAAAAkITQBAAAAEASQhMAAAAASfw//+dMObCz3hYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Histogram for Rating\n",
    "\n",
    "sns.histplot(data['Rating'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "45f57457",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9353, 13)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#6 Price: From the box plot, it seems like there are some apps with very high price. \n",
    "#A price of $200 for an application on the Play Store is very high and suspicious!\n",
    "\n",
    "more = data.apply(lambda x : True\n",
    "            if x['Price'] > 200 else False, axis = 1) \n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "8a70249f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9338, 13)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(data[data['Price'] > 200].index, inplace = True) \n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "798b65d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reviews: Very few apps have very high number of reviews. \n",
    "#These are all star apps that don’t help with the analysis and, in fact, will skew it. \n",
    "#Drop records having more than 2 million reviews.\n",
    "\n",
    "data.drop(data[data['Reviews'] > 2000000].index, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "14e1e1ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8885, 13)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "95c42991",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Size</th>\n",
       "      <th>Installs</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.10</th>\n",
       "      <td>3.5</td>\n",
       "      <td>18.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <td>4.0</td>\n",
       "      <td>159.00</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <td>4.3</td>\n",
       "      <td>4290.00</td>\n",
       "      <td>9500.0</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.70</th>\n",
       "      <td>4.5</td>\n",
       "      <td>35930.40</td>\n",
       "      <td>23000.0</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.90</th>\n",
       "      <td>4.7</td>\n",
       "      <td>296771.00</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>10000000.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.95</th>\n",
       "      <td>4.8</td>\n",
       "      <td>637298.00</td>\n",
       "      <td>68000.0</td>\n",
       "      <td>10000000.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.99</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1462800.88</td>\n",
       "      <td>95000.0</td>\n",
       "      <td>100000000.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Rating     Reviews     Size     Installs  Price\n",
       "0.10     3.5       18.00      0.0       1000.0    0.0\n",
       "0.25     4.0      159.00   2600.0      10000.0    0.0\n",
       "0.50     4.3     4290.00   9500.0     500000.0    0.0\n",
       "0.70     4.5    35930.40  23000.0    1000000.0    0.0\n",
       "0.90     4.7   296771.00  50000.0   10000000.0    0.0\n",
       "0.95     4.8   637298.00  68000.0   10000000.0    1.0\n",
       "0.99     5.0  1462800.88  95000.0  100000000.0    7.0"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#6.3 Installs:  There seems to be some outliers in this field too. \n",
    "#Apps having very high number of installs should be dropped from the analysis.\n",
    "#Find out the different percentiles – 10, 25, 50, 70, 90, 95, 99\n",
    "\n",
    "data.quantile([.1, .25, .5, .70, .90, .95, .99], axis = 0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "a865f104",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping more than 10000000 Installs value\n",
    "\n",
    "data.drop(data[data['Installs'] > 10000000].index, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "cf8fe8e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8496, 13)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "8a5ae28b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#7. Bivariate analysis: Let’s look at how the available predictors relate to the variable of interest, \n",
    "#i.e., our target variable rating. \n",
    "#Make scatter plots (for numeric features) and box plots (for character features) to assess the relations between \n",
    "#rating and the other features.\n",
    "#Make scatter plot/joinplot for Rating vs. Price\n",
    "#What pattern do you observe? Does rating increase with price?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "8c85b036",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Rating', ylabel='Price'>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CAAAAoJDCEQAAAACFFI4AAAAAKKRwBAAAAEAhhSMAAAAACikcAQAAAFBI4QgAAACAQgpHAAAAABRSOAIAAACgkMIRAAAAAIUUjgAAAAAopHAEAAAAQCGFIwAAAAAKKRwBAAAAUEjhCAAAAIBCCkcAAAAAFFI4AgAAAKCQwhEAAAAAheZUOJqamsr/+l//K7/5m7+Z3bt359prr83k5GSjxwYAAABAE82pcHTFFVfk3nvvzV133ZUk+cpXvpKPf/zjDR0YAAAAAM01p8LR7bffnssuuyx9fX1ZunRp/viP/zhf+9rXGj02AAAAAJpoToWj7u7udHY+8629vb3p7u5u2KAAAAAAaL45VX9OPfXUfPazn83k5GS++93v5k/+5E/ykpe8pNFjAwAAAKCJ5nTF0fvf//58+9vfzsjISN761rdm7969ufTSS+cU4PLLL8/73ve+JMmWLVuyfv36nH322bnyyiuf+6gBAAAAaLg5FY6WLl2ad73rXdmyZUv+/u//Pu94xzty9NFHH/bnbr/99tx4441Jkn379uXSSy/N1VdfnS9+8Yv51re+ldtuu+3IRg8AAABAw8ypcHTdddfl3e9+d5LkiSeeyMUXX5zPfe5zh/yZnTt35sorr8ymTZuSJHfddVdOPPHEHH/88enu7s769etzyy23HOHwAQAAAGiUOfU4+su//Mtcf/31SZLjjz8+f/3Xf523vvWtedOb3nTQn/ngBz+Y9773vXn88ceTJNu3b8/g4ODM80NDQ9m2bdu8BzwwsHTeP7NQDQ4ua/YQaAJ5b19y377kvn3JffuS+/Yl9+1J3ttXO+R+ToWjycnJLF36TMFm2bJl6ejoOOj3f+5zn8txxx2X1atXZ/PmzUmSqampWT9Tq9UO+RoHMzKyO1NTtXn/3EIzOLgsw8O7mj0MSibv7Uvu25fcty+5b19y377kvj3Je/uqSu47OzsOeZHOnApHJ510Uj7xiU/kzW9+c5Jk8+bNeeELX3jQ7//iF7+Y4eHhnHvuuXnyySezd+/efO9730tXV9fM9wwPD2doaGiO0wAAAFigOpLRvRP59+8MZ0lfd/oXdyet/3/dAEnmWDj6yEc+kg9/+MN5wxvekO7u7rzyla/Mhz/84YN+/6c//emZf2/evDn//M//nI985CM5++yz89BDD+UFL3hBbr755px//vlHPAEAAICm6UjufvjJXHXDHRmbmExfT1cuueCMnHbCcsUjoBLmVDh63vOel0996lNHFKivry+XXXZZLr744oyNjWXNmjVZu3btEb0mAABAM43unZgpGiXJ2MRkrrrhjlz+7lemf3FPk0cHcOQOWTj62Mc+lve///0zn4z2g6655prDBtiwYUM2bNiQJFm9enVuuumm5zBMAACAhWfn7vGZotEBYxOT2blnXOEIqIRDFo5Wr16dJPnZn/3ZUgYDAADQSlYs60tfT9es4lFfT1dWHNXbxFEB1M8hC0evec1rkiR//dd/nT/90z8tZUAAAACton9xdy654Ixn9TjqX9KjxxFQCXPqcbRr167s3bs3S5YsafR4AAAAWkctOe2E5bn83a/M3onJLOnpUjQCKmVOhaPFixfnrLPOyotf/OJZxaO59DgCAACotFrSv7gnJ5+wMsPDuxSNgEo5bOHo3nvvzU//9E/np37qp3LssceWMSYAAAAAFoBDFo7+6q/+KpdffnlOPPHEPPzww/nEJz6R//Sf/lNZYwMAAACgiQ5ZOLruuuvyN3/zNznmmGPyL//yL7nyyisVjgAAAKqoIxndO5Gdu8ezYllf+hd3u+0OOPytasccc0yS5Mwzz8wTTzzR8AEBAABQso7k7oeffNanw512wnLFI2hznYd6sqOjY9bjrq6uhg4GAACA8o3unZgpGiXJ2MRkrrrhjozunWjyyIBmO2Th6Af9YCEJAACA1rdz9/hM0eiAsYnJ7Nwz3qQRAQvFIW9Vu+eee/Kyl71s5vG+ffvyspe9LLVaLR0dHfnmN7/Z8AECAADQWCuW9aWvp2tW8aivpysrjupt4qiAheCQhaMvfelLZY0DAACAJulf3J1LLjjjWT2O+pf06HEEbe6QhaPnP//5ZY0DAACAZqklp52wPJe/+5XZuWc8K47qVTQCkszhU9UAAABoA7Wkf3FP+hf3zDwGmFdzbAAAAADah8IRAAAAAIUUjgAAAAAopHAEAAAAQCGFIwAAAAAKKRwBAAAAUEjhCAAAAIBCCkcAAAAAFFI4AgAAAKCQwhEAAAAAhRSOAAAAACikcAQAAABAIYUjAACoh85kZPd4tmx9LCN7xv2lDUAldDd7AAAA0PI6kzvv35FrNm/N2MRk+nq6smnDqpx+8spkqtmDA4Dnzv+DAADAERoZHZ8pGiXJ2MRkrtm8NSOj400eGQAcGYUjAAA4QiOj+2aKRgeMTUxmZHRfk0YEAPWhcAQAAEdoYPmi9PV0zfpaX09XBvoXNWlEAFAfCkcAAHCEBpb1ZtOGVTPFowM9jgb6e5s8MgA4MppjAwDAkZpKTj95ZT66cXWe2D2Wo5f2TReNNMYGoMUpHAEAQD1MJQNLe/OSHx7I8PAuRSMAKsGtagAAAAAUUjgCAAAAoJDCEQAAAACFFI4AAAAAKKRwBAAAAEAhhSMAAAAACikcAQAAAFCooYWjT37ykznnnHOybt26fPrTn06SbNmyJevXr8/ZZ5+dK6+8spHhAQAAADgC3Y164X/+53/OP/7jP+amm27K/v37c84552T16tW59NJLc9111+W4447Lxo0bc9ttt2XNmjWNGgYAAAAAz1HDrjj6iZ/4ifzZn/1Zuru7MzIyksnJyYyOjubEE0/M8ccfn+7u7qxfvz633HJLo4YAAAAAwBFo2BVHSdLT05Orrroqf/zHf5y1a9dm+/btGRwcnHl+aGgo27Ztm9drDgwsrfcwm2ZwcFmzh0ATyHv7kvv2JfftS+7bl9y3L7lvT/Levtoh9w0tHCXJJZdckne+853ZtGlTHnzwwXR0dMw8V6vVZj2ei5GR3ZmaqtV7mKUbHFyW4eFdzR4GJZP39iX37Uvu25fcty+5b19y357kvX1VJfednR2HvEinYbeq3X///bn77ruTJIsXL87ZZ5+df/qnf8rw8PDM9wwPD2doaKhRQwAAgKQzGdk9nnsfG83InnGfK0xr6khGn5rIw8N7MrpvfzK//3+fG3sF5ubp/bj1O8ON248LSMOuOHr00Udz1VVX5S/+4i+SJF/+8pdz4YUX5oorrshDDz2UF7zgBbn55ptz/vnnN2oIAAC0u87kzvt35JrNWzM2MZm+nq5s2rAqp5+8Mplq9uBgjjqSux9+MlfdcMfMOr7kgjNy2gnLk3rdjGGvwNyUsR8XmIbVkNesWZNXv/rVecMb3pDzzz8/Z555ZtatW5fLLrssF198cc4555ycdNJJWbt2baOGAABAmxsZHZ95I5wkYxOTuWbz1oyMjjd5ZDB3o3snZt6kJtPr+Kob7sjo3om6xbBXYG7K2I8LTUN7HF188cW5+OKLZ31t9erVuemmmxoZFgAAkiQjo/tm/rg/YGxiMiOj+zKwtLdJo4L52bl7vHAd79wznv7FPXWJYa/A3JSxHxcad60CAFBZA8sXpa+na9bX+nq6MtC/qEkjgvlbsayvcB2vOKp+BR17BeamjP240CgcAQBQWQPLerNpw6qZP/IP9G0Z6K/uH/hUT//i7lxywRmz1vElF5yR/iX1u7rBXoG5KWM/LjQdtVqtpdo3jYzsztRUSw25UFU+to/5kff2JfftS+7bl9wvIJ3T/VtGRvdloH/R9BvhBjb7lfv21dDcd0z3Vtm5ZzwrjuqdfpNa77dFJe+VqrDn29DT+3HvxGSW9HQ1Zj+WqLOzIwMDSw/6fEN7HAEAQNNNJQNLe5/p0+KNMK2olvQv7nmmh0oj3qTaKzA3T+/Hk09YOV00bOGi0Vy4VQ0AAACAQgpHAAAAABRSOAIAAACgkMIRAAAAAIUUjgAAAAAopHAEAAAAQCGFIwAAAAAKKRwBAAAAUEjhCAAAAIBCCkcAAAAAFFI4AgAAAKCQwhEAAAAAhRSOAAAAACikcAQAAABAIYUjAAAAAAopHAEAAABQSOEIAAAAgEIKRwAAACSdycju8dz72GhG9ox7t3g4Xcn2XWP5yh3fy/DusaSr2QOCxuhu9gAAAABoss7kzvt35JrNWzM2MZm+nq5s2rAqp5+8Mplq9uAWoK7kjvt25NobnzleG89blTNOWZlMNntwUF9qyAAAAG1uZHR8pmiUJGMTk7lm89aMjI43eWQL0/adYzNFo2T6eF1749Zs3znW5JFB/SkcAQAAtLmR0X0zRZADxiYmMzK6r0kjWth2HOR47XC8qCCFIwAAgDY3sHxR+npmN+np6+nKQP+iJo1oYTvY8VrpeFFBCkcAAABtbmBZbzZtWDVTDDnQ42igv7fJI1uYBpf3ZeN5s4/XxvNWZWhFX5NHBvWnOTYAAEC7m0pOP3llPrpxdUZG92Wgf9F00Uhj7GKTyRmnrMxHLnpFnhgdy9H9fdNFI42xqSCFIwAAAJKpZGBpbwaW9s485hAmk6FlffmRk56X4eFdikZUllvVAAAAACikcAQAAABAIYUjAAAAAAopHAEAAABQSOEIAAAAgEIKRwAAAAAUUjgCAAAAoJDCEQAAAACFFI4AAAAAKKRwBAAAAEAhhSMAAAAACikcAQAAAFCooYWjT33qU1m3bl3WrVuXK664IkmyZcuWrF+/PmeffXauvPLKRoYHAAAA4Ag0rHC0ZcuWfPWrX82NN96Yv/7rv863v/3t3Hzzzbn00ktz9dVX54tf/GK+9a1v5bbbbmvUEAAAAAA4Ag0rHA0ODuZ973tfent709PTk5NPPjkPPvhgTjzxxBx//PHp7u7O+vXrc8sttzRqCAAAAAAcge5GvfApp5wy8+8HH3wwf/u3f5v//J//cwYHB2e+PjQ0lG3bts3rdQcGltZtjM02OLis2UOgCeS9fcl9+5L79iX37Uvu25fctyd5b1/tkPuGFY4OuO+++7Jx48b8xm/8Rrq6uvLggw/OPFer1dLR0TGv1xsZ2Z2pqVqdR1m+wcFlGR7e1exhUDJ5b19y377kvn3JffuS+/Yl9+1J3ttXVXLf2dlxyIt0Gtoc+xvf+EZ+8Rd/Mb/2a7+W8847L8cee2yGh4dnnh8eHs7Q0FAjhwAAAOXoSEafmsjW7wxndN/+ZH7/Pwq0mirt+afn8vDwnsbNpTMZ2T2eex8bzcie8db+jPcq5X4OGnbF0eOPP573vOc9ufLKK7N69eokyemnn54HHnggDz30UF7wghfk5ptvzvnnn9+oIQAAQDk6krsffjJX3XBHxiYm09fTlUsuOCOnnbA8af2L5YEfVKU9X8ZcOpM779+RazZvnYmxacOqnH7yymSqTjHKUqXcz1HDanx/9Ed/lLGxsVx22WU599xzc+6552bz5s257LLLcvHFF+ecc87JSSedlLVr1zZqCAAAUIrRvRMzbyKSZGxiMlfdcEdG9040eWRAI1Rpz5cxl5HR8Zmi0YEY12zempHR8brFKEuVcj9XDbvi6AMf+EA+8IEPFD530003NSosAACUbufu8Zk3EQeMTUxm557x9C/uadKogEap0p4vYy4jo/sKY4yM7svA0t66xChLlXI/V618VyEAACwIK5b1pa+na9bX+nq6suKo1npDBMxNlfZ8GXMZWL6oMMZA/6K6xShLlXI/VwpHAABwhPoXd+eSC86YeTNxoOdF/5Jq/u8ztLtS93yDG1eXMZeBZb3ZtGHVrBibNqzKQH/rFVva8XzfUavVWqp908jI7kxNtdSQC1XlY/uYH3lvX3LfvuS+fcl9G+qY7n2xd2IyS3q6pt9EtP6frcyDfd9mytjzZTVifnouO/eMZ8VRvY2ZS+d0r6OR0X0Z6F80XTRqtcbYB1TsfN/Z2ZGBgaUHf77EsQAAQHXVkv7FPVl18uB0n4sWfhMBzEEJe760RsxPz+WE5x3VuPPXVDKwtDen/lD/dF+jVi0aJW13vlc4AgAAgAXoUI2YoSwKRwAAALAAtWMjZhYehSMAoH00uMEoANRTOzZiZuHpbvYAAABKUVaDUQCoo97uzpz36hdlqlZLZ0dHertd/0G5FI4AgLZwsAajl7/7ldONLQFggRndO5FP/Pk3Z/U56uvp8ruLUilVAgBtQYNRAFqN310sBApHAEBb0GAUgFbjdxcLgcIRANAWNBiFBUazehqpMxnZPZ57HxvNyJ7xln3n63cXC4EeRwBAe6glp52wPJe/+5XZuWc8K47qnf7DW2NsKJ9m9TRSZ3Ln/TtyzeatM+tr04ZVOf3klclUswc3T353sQC0aN0VAOA5qCX9i3tywvOOmm4q6g9vaIqDNasf3TvR5JFRBSOj4zNFo2R6fV2zeWtGRlu0L5DfXTSZwhEAAFAqDX9ppJHRfYXra2R0X5NGBK1N4QgAACiVhr800sDyRYXra6B/UZNGBK1N4QgAgGrThHnB0fCXRhpY1ptNG1bNWl+bNqzKQL/C5EE5T3IImmMDAFBdmjAvTBr+0khTyeknr8xHN67OyOi+DPQvmi4atVpj7LI4T3IYrjgCAKCyNGFewDT8pZGmkoGlvTn1h/ozsFTR6FCcJzkchSMAACpLE2aAQ3Oe5HAUjgAAqCxNmAEOzXmSw9HjCACAyjrQhPkHe3fop9MmOqZvw9m5ezwrlvWlf3F3Y/LemYyMjue+f38sK5f1ZWBZA26NejrGyOi+DCxf1JgYZSkjL2XlvgwNnkv/4u781tt/LHvHJvPU2P4sXtSdJb1d9T9PViknbUbhCACA6tKEuX2V1fC3M7nz/h25ZvPWmTibNqzK6SevrF9hp4wYZSkjL1Vq9lzS8Xpi99iz1tcJxxwlJyRxqxoAAFWnCXNbKqvh78jo+Mwb7gNxrtm8NSOj9esPU0aMspSRlyo1ey5jLmWsryrlpB0pHAEAAJVTVsPfkdF9hXFGRve1VIyylJGXKjV7LmMuZayvKuWkHSkcAQAAlVNWw9+B5YsK4wz0L2qpGGUpIy9VavZcxlzKWF9Vykk7UjgCAAAq50Bj9ANvVmc1Rq+jgWW92bRh1aw4mzasykB/Hd/YlxCjLP2Lu/Prb31ZLnzti3PBz5yaC1/74vz6W19W17yUlfsylDGXMtZXlXLSjjpqtVpL3eU9MrI7U1MtNeRCg4PLMjy8q9nDoGTy3r7kvn3JffuS+/Yl9wvIgU9xanRj9Kc/8eyJ3WM5emnf9BvuRn6qWv+ixsQoQ1lNksvKfUrY82XMpYz1VWJOylKV831nZ0cGBpYe9HmfqgYAAFTT043R+xf3zDxuiKlkYGlvXvLDA9NvIhtR0Hk6xsDS3pnHrehgTZIvf/crn8lTPZSV+zKUMZcy1leVctJm3KoGAABAKTRJhtajcAQAAEApNEmG1qNwBAAAraIjGX1qIg8P78novv1JR7MHBPNTWpPkzmRk93jufWw0I3vGW/udr31Pk+lxBAAAraCspsLQYL3dnTnv1S/KVK2Wzo6O9HbXuarTmdx5/45cs3nrzF7ZtGFVTj95Zev1hrLvWQBaue4KAABt42BNhUf3TjR5ZDB3o3sn8ok//2au/9I9ueHv7831X7onn/jzb9Z1HY+Mjs8UjZLpvXLN5q0ZGW29Pkr2PQuBwhEAALQATYWpgjLW8cjovsIYI6P76hajLPY9C4HCEQAAtABNhamCMtbxwPJFhTEG+hfVLUZZ7HsWAoUjAABoAaU1FWZhqkiz5zLW8cCy3mzasGpWjE0bVmWgv87Flq5k+66xfOWO72V491jSdfgfmS/7noWgo1artVRLrZGR3ZmaaqkhFxocXJbh4V3NHgYlk/f2JfftS+7bl9y3r4bmvmO658nOPeNZcVTv9JvH1v/TuDIalvsqNXtOylnHndO9jkZG92Wgf9F00aiex6orueO+Hbn2xmdysvG8VTnjlJXJ5OF/fF7s+wWrKr/rOzs7MjCw9ODPlzgWAADgSNSS/sU9OeF5R6V/sTeP7aJKzZ6TlLOOp5KBpb059Yf6M7C0zkWjJNt3js0UjZLpnFx749Zs3zlW30CJfU/TKRwBAAAsYFVq9lwVOw6Skx1yQgUpHAEAACxgVWr2XBUHy8lKOaGCGlo42r17d17/+tfn0UcfTZJs2bIl69evz9lnn50rr7yykaEBAGBaRzL61EQeHt6T0X37k45mD+gIVGkuzFlpzZ6Zs8Hlfdn4AznZuGFVhlb0NXlkC5jzV8vqbtQL33nnnfnABz6QBx98MEmyb9++XHrppbnuuuty3HHHZePGjbntttuyZs2aRg0BAIB215Hc/fCTueqGO2Ya2F5ywRk57YTlrdcnpEpzYX6mktNPXpmPblzduGbPzE8t6V/Sk19585l5anx/Fvd2p7enw148GOevltawK45uuOGGfOhDH8rQ0FCS5K677sqJJ56Y448/Pt3d3Vm/fn1uueWWRoUHAICM7p2YeaOSTPcgueqGOzK6d6LJI5u/Ks2F56DBzZ6Zn5HR8fzeZ76ZKz7z9fzBDXfkis98Pb/3mW+2bsPyBnP+am0Nu+LoYx/72KzH27dvz+Dg4MzjoaGhbNu2bd6ve6iPiGs1g4PLmj0EmkDe25fcty+5b19y33z//p3hwga2eycmc/IJKxsWtxG5b9ZcmB/7vj3c9++PFe7HJ3aP5SU/PNCkUS1cVT5/tcOeb1jh6AdNTU2lo+OZmxhrtdqsx3M1MrI7U1Otfy3b4OCyDA/vavYwKJm8ty+5b19y377kfmFY0tedvp6uWW9Y+nq6sqSnq2H5aVTumzEX5se+bx8rl/UV7sejl/ZZAwWqev6qyp7v7Ow45EU6pX2q2rHHHpvh4eGZx8PDwzO3sQEAz4Emk1RBg9dx/+LuXHLBGbMa2F5ywRnpX9JT30AlqNJckjiHLUTdybbRsdz96JPZvmuscZcZdCYju8dz72OjGdkz3ph3pQ1eXwPLevMrF56RC1/74lzwM6fmwte+OL9y4RmNaVhegeNVufNXmyntiqPTTz89DzzwQB566KG84AUvyM0335zzzz+/rPAAUC2aTFIFZazjWnLaCctz+btfmZ17xrPiqN7pNyqtuE+qNBfnsIWnO7nj3h259satMznZeN6qnHHqymR/HeN0JnfevyPXbH4mzqYNq3L6ySvr17epjPXVmYyNT+bG//OdWfNIZ+rbf6oqxytJb3dnznv1izJVq6WzoyO93aVdx8IRKi1TfX19ueyyy3LxxRfnnHPOyUknnZS1a9eWFR4AKkWTSaqgtHVcS/oX9+SE5x2V/sUtWmg5oCJzcQ5beLbtGJspGiXTObn2xq3ZtmOsrnFGRsdniiAH4lyzeWtdm0qXsb627RgrnIfjdfAYn/jzb+b6L92TG/7+3lz/pXvyiT//pj3fIhp+xdGtt9468+/Vq1fnpptuanRIAKi8nbvHC5tM7twzPv1mElqAddy+5H7h2TG6r7jZ8659Oaa/r25xRg4SZ2R03/SnxdVBGevL8Vp4MWgc14YBQAta8XRTzu/X19OVFUc1oLcCNIh13L7kfuEZWL6oMCdHL1tUSpyB/vrFKWN9OV4LLwaNo3AEAC1Ik8k2V5Gmwtbxc1BG7kuIUVruK7JXyjB0dF82nrdqVk42nrcqx6ys39UzyXRT6Xed/9JZcd51/kvr2lS6jPVV5vHatGF2nE0bVrXc8XK+b20dtVqtpe6MHhnZnamplhpyoap8bB/zI+/tS+7bV0Nz3zHdM6Dlm+RWVMNyX7WmwhVcxy2d+zLXV6Nz34S90vK/77une/c8sWtfjl62aLoIUs/G2EnSkdz/2K7c/9joTJPkk3+oPyf/0LK657/h55Yyjlcy/alqo+PTt6f1L5ouGtWzAXdSzvFyvl+wOjs7MjCw9KDPKxw1SVUWGPMj7+1L7tuX3LevRuV+9KmJ/ObVW2b1iujr6crl736lPhELRCvnvkrrqxlzcc4/vCqtsQPkvX1VJfeHKxy5VQ0AoIUcqsEo1VZG7qu0vqo0lyqRF2g9CkcAAC1Eg9H2pYHt/FRpLlUiL9B6FI4AAFqIBqML2NONmLd+Z7ghjZg1sJ2fKs2lNFVqjN6ZjOwez72PjWZkz3hj3vl2Jdt3jeUrd3wvw7vHkq7D/8hzUpGm+LQuPY6apCr3QjI/8t6+5L59yX370hi9zZTViFkD2/kpeS4tfc6vUmP0zuTO+3fkms1bZ+ayacOqnH7yyvo1le5K7rhvR6698ZkYG89blTNOWZlMHv7H56xqTfErpqX3/PfR4wgAoGpqSf/inpzwvKOmm8n6w77pRvdOzLzpSqZ7tlx1wx0Z3TtR30Bl5L5K66tKc2mw0tZw0vC8jIyOzxSNkum5XLN5a0ZG69dHafvOsZmi0YEY1964Ndt3jtUtRlJOXkrNPS1J4QgAAI6Qhr+0uiqt4ZHRfYVzGRndV7cYOw4SY0cdYySa4rMwKBwBAMAR0vCXVlelNTywfFHhXAb6FzU8xso6xkg0xWdhUDgCgFalkSXMTZUa/laJc9j8NPh4VWkNDyzrzbvOf+msubzr/JdmoL9+hZDB5X3ZtGHVrBibNqzK0Iq+usVIymuK/+tvfVkufO2Lc8HPnJoLX/vi/PpbX1b/3D/dTPzfvvdkY5uJU3fdzR4AAPAcaGQJc1PWXqklp52wPJe/+5XZOzGZJT1drd1UutGcw+anpOPV292Z8179okzVauns6Ehvd4teZ1BLjurrnjWXo/q667u2ppKli3pmxVi6qKd+zbe/Txl5Gd8/lRv/z3dmra+6KquZOA3hU9WapCrd15kfeW9fct++GpX70acm8ptXb5nVk6CvpyuXv/uV041GaTr7fmFoxl6R+8Or6jmslc/5VcpJlY5XVeayfddYPvQ//vFZMT5y0SsytKy+V2mVqSrne5+qBgAVpJElzI29sjDJy/xokDw/VTpeVZlLWc3EaQyFIwBoQRpZwtzYKwuTvMyPBsnzU6XjVZW5lNVMnMZQOAKAFlSlJqa0OQ1/21JpealIA+6yGiT/9i/9eD7yzlfk1976snzkolfkt3/px1tyr/Qv7s4lb/6B4/Xm+h+vMtZwVeYyuLwvG8+b3Ux843n1byZOY2iODQCt6Psa8e7cM54VR/VqxEvrKaPhr72yYDW84W+VGnCXsY47k+079z2refEPDS5pvebFXUmtVpu1vmq12vSneO2vU4yOJB2zY6SjNv31eual+yBz6U4yUacYZcxlMjnjlJX5yEWvyI7RfVnZv2i6aNRqa6tNKRwBQKuqJf2Le55pXNlqb4Roe6N7J2be1CfT/S6uuuGO+jfjtVcWnNG9E/nEn3+zsQ1/y1pfZWnwOt6+c2ymaJRMH69rb9zaks2Lt+0Yyx/ccOez1teH3/mKHNNfn7mMjI7nqr98doyPblydgaX1u8Vr20h15pLJZGhZ3zPrSdGoZbhVDQCApqhSM17mpyoNf6ukSs2LDzaXJ3bVby4jB4kxUufjVaW50LoUjgAAaIoqNeNlfqrS8LdKqtS8+GBzOXpZ/eZysBgDdT5eVZoLraujVqu11MW6IyO7MzXVUkMuNDi4LMPDu5o9DEpWibx3TF/6vXP3eFYs60v/4m6X/B/K08dr7/hklvR1O15tyL5fQDGSpGv6dowdo/sysHxRBpc3oL9C5/Rl/zt2j2Xlsr4MLOtNpuocoyo6koe378nesck8NbY/ixd1Z0lvV04YOqq++S9rfVXlnP/0Gh55ep80ZA13JPc/tiv3PzY601Pl5B/qz8k/tKx+x6wjeWxkbyZryeie8fQv7U1Xkh8aWFL/vJSx73umb1s6cP4aWtlXvx43SdKV3PmdHblm8zM9jjZtWJXTX7Sy/ufJRp+Lu5N7H34yDz6+e2Z9vfC4pTn1hOX163HUmWwbfSq1yc48sWu6Z086p3JM/+L65r4neWx4bzrSmZ27x7JiWV9qtanp3lP1yn9n8tiOghgrl9R9Lg1dwweUcc6fWcNjGVje15i/J0rU2dmRgYGlB31ejyNg7qrUZLIMjhdVUMY6LmuvdCV33LfjWY1fzziljm+KOpM77y9443XySsWjIh3JE7vHnnW8TjimjoWjstZXVc75Za3hjmTP2P7c+H++MxPnXee/tL7NeLsO0ux5aEn9igdJOcesJ7njnoLz14tX1rVBcl9v16wGyX29XfX/JLoyzsUdyVNjk7PW16YNq+o7l67k8e1PPWsexxxd58JRku07DtK0vF66DhGjldZwUs65uIw1vMC4VQ2Ys4M1mRzd24j/Kmh9jhdVUMY6LmuvHKzx6/adY3WLMTI6PvPm8UCMazZvzcionipFyjheZa2vqpzzy1rDI6Pj+cO/umtWnD/8q7vqGmfbjuI9v21H/fZ8Us4x2zZykLmM1G8u23aM5ZPX35Hrv3RPbvj7e3P9l+7JJ6+/o+7Hq4xz8baRscKc1PV4lZCTsuJUJUZSzrm4jDW80CgcAXOmyeT8OF5UQZUa2JbR+FWD0fkp43iVtb6qcs4vaw2XEaeMpsJJdeZS1vEq41zcDser1eZS1vEq41xcpUbyc6VwBMyZJpPz43hRBVVqYFtG41cNRuenjONV1vqqyjm/2Q1/6xmnjKbCh4rTanNp9vEq41xcpePVanMp63iVcS6uUiP5udIcu0kq0SyVeWv5vFelf0NZHK+Fq8Rmzy3fJFePo/npTO767hMzt+Ac6Nvy0pOObs0eR43eK2X0hqlaj6Mq5OTpOP/2yJN5+N+faV58wrFL85Ljl9cvTnfyrw/szKPb98zEeMHQUfl/fnhF3Xsc3f3wzjyy7Zk4xx9zVE47YUVd+8PceW9BXk6tY3+Y7uTfHirIyYl1bCidTJ+Lv7Mj137fXDZuWJUz6tmEuyd5+PHd6ejonGmMXpuaygnHLa3f8ep5en19X95fcMzT66ued6iW0RuoJ7nrvoLfXaccXdcY//Zgwfp64fK69zhqeOP9ruRfHyw4t7xwRcv2ODpcc2yFoyZp+QICz0kl8n7gD9Y941lxVG/6l/S05pvhshwoHkxMZklPl+O1EFSpEFKWMvZ9WeeW7/skn5X9izK0os6fglLGH6xlKWsdf/8nePUvykB/Yz7Bq5T11ehzfpVyUsYneHUkdz/yZK76y+87Xm8+I6cdX//j1fCCcWdyz6NP5qHv+5SwE49bmhe/oI6FtjKKU0/H+c7Do9k/mTw1vj+Le7vT3ZW86IT+uhYpKlFseTrOQ4/vTmdHZ0b3jqd/SW+malM5sd5FsO8WFEJOWlHXwuSd9xWsr1NW1r2QW8Z+3PrAE7n688/EePcbX5pVP9yi/0kUhaMFqxIFBOZN3tuX3C8co09N5Dev3jLr3vS+nq5c/u5Xpn9xT8vEYGGqUu6rNJeyNeqcX6WcbN81lg/9j3981lw+ctErMrSsry4xyjpeI7vH84Frb39WnI9uXJ2BpfW5PaaMGNtGx/Lh//nsnHz4na/IMf31yUlZcaoSo6w4VYmRVGc/lu1whSM9jgBoK1Vq9szCU6XcV2kuVVGlnJTRXLas41VGc+yqNOAuK05VYpQVpyoxkursx4VG4QiAtlKlZs8sPFXKfZXmUhVVykkZzWWb3Xi/jObYrdaAu6w4VYlRVpyqxDhUnFbbjwuNW9XKVpVmqTwnbldqX3K/gOhxNH9lNBOvCj2OFqbv6201sHxRBpfXubfVAY3+O68jeXj7nuwdm8xTY/uzeFF3lvR25YSho+obpzvZtuOZ4zV0dF99e5AkpfU4KmU/ltHou4wYPcm3v7szn7rhzpmc/P8uOD0/Us8+N0/H+e73dmV8vDazjnt7OnLS85fVtxHzQ0/m4e/rCXXCcU83+m61Zs89yY5dY5kYn75qZ+XyRenpSVYu66vrXO5/ZDQT+zOTk56u5OTj69h3qju5496CvlOn1r/HUcOb1Zf1IQIlOtytat0ljoUq/QEG0KpqyWknLM/l735l4xrlfl+Mlm+M7nfX/HQmu/dN5Mb/851Zf0ymM633SStl7JUylPFpekk5e6Uz2bF77FmfRnXCsUfVby4lvrnr7enMea9+0cybu96ezvrula6D7Meu1L0QNj4xNSvOu85/aX0DdCVj45PFc6njG9WOZFZOOur30rOM7p541hqrt7GxguNV7xhFOWmAhx/b86zjtfLF9esLlCS79u5vbE46kqOX9ebSX/zxZ5p8T02l7ouslqTWMSsvl1xwRt1/dy1d3J1fefOZMw3eeypeWXHFUYmq1NCQ58ZVJ+1L7ttXq+fe7675KaPhL/NTVk7K2CtlzEXD3/mrSuNquV94McqKU5UYSTnnYs2xaagqNTQEoD343TU/ZTT8ZX7KykkZe6WMuWj4O39VaVwt9wsvRllxqhIjKedcrDk2DVWlhoYAtAe/u+anjIa/zE9ZOSljr5Qxl2Y3sNWMd34xqnS8Wm0ujtfCi5E091ysOfYC0sq3qlWqYSbPSavfslKqijXjlfs5KivvJa6vls+9Hkfz05Xc8Z0dz+pBc0Y9G/4m1TpHNnouZTRhTsrZK2Wsr7J6HPUkd9xTEOfFK1uyGe+3Hnwi//1zd83Eec+bXpr/94VH17VxdcOPV0/ynYdHs3/ymQbJ3Z3Ji06oY4Pkp+Pc/UBB8+IfXlHXudx5b8G+P7W+x6vh83g6ThlzaXij755k246nUpvqzBO79k0XvDumcszKxfU9XiX1m/uPXU9lcrJzpmF5V+dUnrdssebY1Mf4/qlnN+oCZvNGtT2VlXfra36q0iC5LFPJyqV9sxpmLunrqu8fklVaw2XMZSpZuqhnVsPfpYt6GvLHfW/3DzR77q7zxf1lrK/J5LiBxfng//fy7Nw9lhVL+9LZUat/c/f9058K9ay51LOgU0tW9s9uxlurTdV/n3QlnR0ds3Lf2dFR38bVtWRged+suSS1us9l9779zypMNsIPNhNvRFPpvt6uWTnp6+06/A/NUxnzSMqZSxmNvh8ffupZxc9jVi6ue5yGn4u7kke3PXsuz1vRuoWjw3HFUYk0GKXlrzwoSRX3itwfXll5L3t9yX17KWN9VekcWaXjVZW5VOl4ldUYvSqNhTV7XngxyopTlRhJOeeWsuZSJs2xFxANRmFu7JX2VFberS8aqYz1VaU1XKXjVZW5VOl4ldUYvSqNhTV7XngxyopTlRhJc88t9Z7LQqJwVCINRmFu7JX2VFberS8aqYz1VaU1XKXjVZW5VOl4ldUYvSqNhTV7XngxyopTlRhJc88t9Z7LQtKUW9X+5m/+Jn/4h3+Y/fv35xd+4Rfytre9bc4/28q3qqUjeWpqMqO79s800epf2p3FnV0t2ZOgMk05u5LtO8eyY3RfBpYvyuDyvvrfxz8TYywDy/saEyNJupNtO56Zy9DRffXtFZBMN7Yb+b4YK/vq29AuSTqSnU+NZ2y8NrNX+no6smJxb33XWRlzKSNGWXEaHaMjufuRJ3PVX35fr5M3n5HTjq9/j6OnapMZHf2+c/Gy7izuqPO5uAo5KTNOVebSkUx2T+Y/Rp5ZX89b2Z2u/XVcX2XEOMDxWlhxqhKjrDhdyWRnQYyprvr+HdaTTKYgTrrq2uh7sqMgRq2OfaF6pq+kmJjomInR012bLrTVudnzUxMFv4d76ni8yshJT7K/NpmRHc/EGDi6O90ddYzxdJzxqck88cQzcY5e0Z3ezvrOZfe+iezZMzUT46glndP94OoYo5T1Vca5pYz1VbIF1xx727ZtufLKK7N58+b09vbmwgsvzMtf/vK86EUvKnso5etO7rnnycZ+GkIZqtSUsyu5476CT6g4pY6fTlJGjKScTw4p4xM9kqQ7efCx3Q3/5JAyPp2klONVlbl0J7VabVYzw1qtNv2bqs7rq+Hn4qrkpKw4VZpLd7K10eurjBiJ47UQ41QlRllxOg8Ro87/gXfQOPXSUUKMJA8/vvdZMep9hVaS3PPdxs+ljOP1rXsbHyNJ/vW+xsf5zkO7qrG+SjqHlbG+FpLSb1XbsmVLXvGKV2TFihVZsmRJfvZnfza33HJL2cNoim0jYzOLK5m+D/LaG7dm28hYk0c2P6N7J2aKRsn0PK664Y6M7m2l6te07TuLc7J9Z/1yUkaMZPpKo8L1taN+ccpaw2XEqUqMsuKUFeMPbrgz13/pntzw9/fm+i/dkz+44U7Hq4kxyopjLgsvRllxqhKjrDhViVFWHHNZeDHKilOVGGXFqUqMsuJU5X39fJR+xdH27dszODg483hoaCh33XXXnH/+UJdPLXR3P/rkQZto/b8nP69Jo5q/f//OcOE89k5M5uQTWqvK+m/fGy3OyehYfuSk+uSkjBhJOeurrDVclbk4XgsvRllxqhKjrDjmsvBilBWnKjHKilOVGGXFMZeFF6OsOFWJUVacqsQoK05V3tfPR+mFo6mpqXR0dMw8rtVqsx4fTiv3ODrQROv7F9mBJlqt9FHNS/q6C+expKerpeaRJAPL+4pz0t9Xt7mUEWM6TuPXV1lruCpzcbwWXoyy4lQlRllxzGXhxSgrTlVilBWnKjHKimMuCy9GWXGqEqOsOFWJUVacqryv/36H63FU+q1qxx57bIaHh2ceDw8PZ2hoqOxhNMXQyr5sPG/VTAf2A/dCHjPQ1+SRzU//4u5ccsEZs+ZxyQVnpH9JT5NHNn+Dy4tzMrSifjkpI0aSDB19kPW1sn5xylrDZcSpSoyy4lQlRllxqhKjrDjmsvBilBWnKjHKilOVGGXFMZeFF6OsOFWJUVacqsQoK05V3tfPR+mfqrZt27a85S1vyec///ksXrw4F154YX73d383L33pS+f08618xVGSmU8neWLXvhy9bNH04mq91kDPfKranvGsOKp3umjUqmn5vk9VW9m/aLqg06BPVXtidCxH9/c1JkYy86lqM+trZeM+Va3ha7iMOFWJUVacqsQoK05VYpQVx1wWXoyy4lQlRllxqhKjrDjmsvBilBWnKjHKilOVGGXFqcr7+qcd7oqj0gtHSfI3f/M3ufbaazMxMZE3vvGNeec73znnn235wtHTBgeXtexlbDx38t6+5L59yX37kvv2JfftS+7bk7y3r6rk/nCFo9J7HCXJ+vXrs379+maEBgAAAGCOSu9xBAAAAEBrUDgCAAAAoJDCEQAAAACFFI4AAAAAKKRwBAAAAEAhhSMAAAAACikcAQAAAFBI4QgAAACAQgpHAAAAABRSOAIAAACgkMIRAAAAAIUUjgAAAAAopHAEAAAAQKHuZg9gvjo7O5o9hLqp0lyYO3lvX3LfvuS+fcl9+5L79iX37Une21cVcn+4OXTUarVaSWMBAAAAoIW4VQ0AAACAQgpHAAAAABRSOAIAAACgkMIRAAAAAIUUjgAAAAAopHAEAAAAQCGFIwAAAAAKKRwBAAAAUEjhCAAAAIBCCkcAAAAAFFI4arDdu3fn9a9/fR599NFnPXf33Xdnw4YN+dmf/dm8//3vz/79+5swQhrlULn/1Kc+lbPOOivnnntuzj333Hz2s59twghphE996lNZt25d1q1blyuuuOJZz9v31XW43Nv31fXJT34y55xzTtatW5dPf/rTz3revq+uw+Xevq+2yy+/PO973/ue9XV7vvoOlnt7vrp+/ud/PuvWrZvJ7Z133jnr+crv+xoNc8cdd9Re//rX137kR36k9sgjjzzr+XXr1tX+5V/+pVar1Wq/9Vu/VfvsZz9b8ghplMPlfuPGjbVvfvObTRgZjfS1r32t9uY3v7k2NjZWGx8fr7397W+v/d3f/d2s77Hvq2kuubfvq+mf/umfahdeeGFtYmKi9tRTT9XOOuus2v333z/re+z7appL7u376tqyZUvt5S9/ee03f/M3n/WcPV9th8q9PV9NU1NTtZ/6qZ+qTUxMHPR7qr7vXXHUQDfccEM+9KEPZWho6FnPfe9738u+fftyxhlnJEk2bNiQW265peQR0iiHyn2SfOtb38q1116b9evX53d+53cyNjZW8ghphMHBwbzvfe9Lb29venp6cvLJJ+exxx6bed6+r67D5T6x76vqJ37iJ/Jnf/Zn6e7uzsjISCYnJ7NkyZKZ5+376jpc7hP7vqp27tyZK6+8Mps2bXrWc/Z8tR0q94k9X1Xf/e53kyTveMc78nM/93P5zGc+M+v5dtj3CkcN9LGPfSw/9mM/Vvjc9u3bMzg4OPN4cHAw27ZtK2toNNihcr9nz56cdtpp+a//9b/mxhtvzOjoaK6++uqSR0gjnHLKKTO/MB588MH87d/+bdasWTPzvH1fXYfLvX1fbT09Pbnqqquybt26rF69Osccc8zMc/Z9tR0q9/Z9dX3wgx/Me9/73vT39z/rOXu+2g6Ve3u+ukZHR7N69er89//+3/Mnf/Inuf766/O1r31t5vl22PcKR00yNTWVjo6Omce1Wm3WY6rrqKOOyv/8n/8zJ598crq7u/OOd7wjt912W7OHRR3dd999ecc73pHf+I3fyAtf+MKZr9v31Xew3Nv31XfJJZfk9ttvz+OPP54bbrhh5uv2ffUdLPf2fTV97nOfy3HHHZfVq1cXPm/PV9fhcm/PV9eZZ56ZK664IsuWLcvKlSvzxje+cVZu22HfKxw1ybHHHpvh4eGZx//xH/9x0NuaqJbHHnssn//852ce12q1dHd3N3FE1NM3vvGN/OIv/mJ+7dd+Leedd96s5+z7ajtU7u376rr//vtz9913J0kWL16cs88+O/fcc8/M8/Z9dR0u9/Z9NX3xi1/M1772tZx77rm56qqrcuutt+a//bf/NvO8PV9dh8u9PV9dX//613P77bfPPP7B3LbDvlc4apLnP//56evryze+8Y0kyRe+8IW86lWvavKoKMOiRYvye7/3e3nkkUdSq9Xy2c9+Nq997WubPSzq4PHHH8973vOefOITn8i6deue9bx9X12Hy719X12PPvpoPvCBD2R8fDzj4+P58pe/nB/90R+ded6+r67D5d6+r6ZPf/rTufnmm/OFL3whl1xySV7zmtfk0ksvnXnenq+uw+Xenq+uXbt25YorrsjY2Fh2796dG2+8cVZu22HfKxyV7J3vfGe2bt2aJPnEJz6Rj3/841m7dm327t2bt7/97U0eHY10IPcrV67M7/zO7+Rd73pX1q5dm1qtll/6pV9q9vCogz/6oz/K2NhYLrvsspmP6vyLv/gL+74NHC739n11rVmzJq9+9avzhje8Ieeff37OPPPMrFu3zr5vA4fLvX3fXuz59mXPV99ZZ52VNWvWzJzvD5zz22nfd9RqtVqzBwEAAADAwuOKIwAAAAAKKRwBAAAAUEjhCAAAAIBCCkcAAAAAFFI4AgAAAKBQd7MHAACwEL34xS/Oqaeems7OznR0dOSpp57K0qVL8+EPfzirVq065M9+7nOfy/j4eN72trflL/7iL7Jr165cdNFFJY0cAKB+FI4AAA7iT//0T7Ny5cqZx3/0R3+Uj370o/nLv/zLQ/7cN77xjZxyyilJkre85S0NHSMAQCMpHAEAzMH+/fvz+OOPZ/ny5UmS//iP/8gHP/jBjIyMZHh4OM9//vPz+7//+/nmN7+ZW2+9NV/72teyaNGi7NixI0888UQ++MEP5jWveU3OO++83H777Xn88cdz7rnn5ld/9VeTJP/jf/yPfP7zn89RRx2VH/uxH8uXv/zl3HrrrU2cMQCAwhEAwEH9wi/8QpLkiSeeSF9fX84666x8/OMfT5L87//9v3PGGWfkoosuSq1Wy0UXXZQvfOELecc73pEvf/nLOeWUU/K2t70tf/AHfzDrNffu3Zs///M/z7Zt2/La1742559/fh588MFs3rw5n//857Ns2bK8//3vL32uAABFFI4AAA7iwK1q3/72t3PRRRfl5S9/eQYGBpJMF5W+/vWv59Of/nQefPDB3HfffTn99NMP+5o//dM/nSQ55phjMjAwkCeffDK33XZb1q5dm/7+/iTJ2972tvzjP/5j4yYGADBHCkcAAIfxIz/yI/mt3/qtvO9978tpp52WF7zgBfm93/u93HXXXTn//PPz8pe/PPv370+tVjvsa/X19c38u6OjI7VaLd3d3bN+tqurqyHzAACYr85mDwAAoBW8/vWvz0tf+tKZW9W++tWv5hd+4Rfyhje8IQMDA9myZUsmJyeTTBd+9u/fP+fXXrNmTf7u7/4uu3btSpJ8/vOfr/8EAACeA1ccAQDM0W//9m/n537u5/KVr3wl73nPe3LFFVfkk5/8ZHp6evKyl70sDz/8cJLkVa96VS677LI5v+7q1atzwQUX5M1vfnMWLVqUU045JYsXL27UNAAA5qyjNpdrqgEAaJitW7fmX/7lX/L2t789SfLpT386d955Z37/93+/uQMDANqewhEAQJPt3r07l156ab773e+mo6Mjxx13XH73d383xxxzTLOHBgC0OYUjAAAAAAppjg0AAABAIYUjAAAAAAopHAEAAABQSOEIAAAAgEIKRwAAAAAU+v8DawIs3U8fl70AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='Rating',y='Price',data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "cea874b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Rating', ylabel='Installs'>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='Rating',y='Installs',data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "27102c05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Rating', ylabel='Size'>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='Rating',y='Size',data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "e4215532",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Rating', ylabel='Content Rating'>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"Rating\", y=\"Content Rating\", data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "79e15f93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Rating', ylabel='Category'>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"Rating\", y=\"Category\", data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "e7c98186",
   "metadata": {},
   "outputs": [],
   "source": [
    "inp1 = data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "0b89db91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>App</th>\n",
       "      <th>Category</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Size</th>\n",
       "      <th>Installs</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Content Rating</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Last Updated</th>\n",
       "      <th>Current Ver</th>\n",
       "      <th>Android Ver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Photo Editor &amp; Candy Camera &amp; Grid &amp; ScrapBook</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.1</td>\n",
       "      <td>159.0</td>\n",
       "      <td>19000.0</td>\n",
       "      <td>10000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>January 7, 2018</td>\n",
       "      <td>1.0.0</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Coloring book moana</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>3.9</td>\n",
       "      <td>967.0</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>500000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design;Pretend Play</td>\n",
       "      <td>January 15, 2018</td>\n",
       "      <td>2.0.0</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U Launcher Lite – FREE Live Cool Themes, Hide ...</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.7</td>\n",
       "      <td>87510.0</td>\n",
       "      <td>8700.0</td>\n",
       "      <td>5000000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>August 1, 2018</td>\n",
       "      <td>1.2.4</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pixel Draw - Number Art Coloring Book</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.3</td>\n",
       "      <td>967.0</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>100000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design;Creativity</td>\n",
       "      <td>June 20, 2018</td>\n",
       "      <td>1.1</td>\n",
       "      <td>4.4 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Paper flowers instructions</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.4</td>\n",
       "      <td>167.0</td>\n",
       "      <td>5600.0</td>\n",
       "      <td>50000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>March 26, 2017</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.3 and up</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 App        Category  Rating  \\\n",
       "0     Photo Editor & Candy Camera & Grid & ScrapBook  ART_AND_DESIGN     4.1   \n",
       "1                                Coloring book moana  ART_AND_DESIGN     3.9   \n",
       "2  U Launcher Lite – FREE Live Cool Themes, Hide ...  ART_AND_DESIGN     4.7   \n",
       "4              Pixel Draw - Number Art Coloring Book  ART_AND_DESIGN     4.3   \n",
       "5                         Paper flowers instructions  ART_AND_DESIGN     4.4   \n",
       "\n",
       "   Reviews     Size  Installs  Type  Price Content Rating  \\\n",
       "0    159.0  19000.0     10000  Free      0       Everyone   \n",
       "1    967.0  14000.0    500000  Free      0       Everyone   \n",
       "2  87510.0   8700.0   5000000  Free      0       Everyone   \n",
       "4    967.0   2800.0    100000  Free      0       Everyone   \n",
       "5    167.0   5600.0     50000  Free      0       Everyone   \n",
       "\n",
       "                      Genres      Last Updated Current Ver   Android Ver  \n",
       "0               Art & Design   January 7, 2018       1.0.0  4.0.3 and up  \n",
       "1  Art & Design;Pretend Play  January 15, 2018       2.0.0  4.0.3 and up  \n",
       "2               Art & Design    August 1, 2018       1.2.4  4.0.3 and up  \n",
       "4    Art & Design;Creativity     June 20, 2018         1.1    4.4 and up  \n",
       "5               Art & Design    March 26, 2017         1.0    2.3 and up  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "a9122d60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rating      -1.749753\n",
       "Reviews      4.576494\n",
       "Size         1.655917\n",
       "Installs     1.543697\n",
       "Price       18.074542\n",
       "dtype: float64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp1.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "598dd92c",
   "metadata": {},
   "outputs": [],
   "source": [
    "reviewskew = np.log1p(inp1['Reviews'])\n",
    "inp1['Reviews'] = reviewskew"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "e6e038c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.20039949659264134"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviewskew.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "de754826",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           10000\n",
       "1          500000\n",
       "2         5000000\n",
       "4          100000\n",
       "5           50000\n",
       "           ...   \n",
       "10834         500\n",
       "10836        5000\n",
       "10837         100\n",
       "10839        1000\n",
       "10840    10000000\n",
       "Name: Installs, Length: 8496, dtype: int32"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "installsskew = np.log1p(inp1['Installs'])\n",
    "inp1['Installs'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "065b251c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.5097286542754812"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "installsskew.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "15377726",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>App</th>\n",
       "      <th>Category</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Size</th>\n",
       "      <th>Installs</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Content Rating</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Last Updated</th>\n",
       "      <th>Current Ver</th>\n",
       "      <th>Android Ver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Photo Editor &amp; Candy Camera &amp; Grid &amp; ScrapBook</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.1</td>\n",
       "      <td>5.075174</td>\n",
       "      <td>19000.0</td>\n",
       "      <td>10000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>January 7, 2018</td>\n",
       "      <td>1.0.0</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Coloring book moana</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>3.9</td>\n",
       "      <td>6.875232</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>500000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design;Pretend Play</td>\n",
       "      <td>January 15, 2018</td>\n",
       "      <td>2.0.0</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U Launcher Lite – FREE Live Cool Themes, Hide ...</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.7</td>\n",
       "      <td>11.379520</td>\n",
       "      <td>8700.0</td>\n",
       "      <td>5000000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>August 1, 2018</td>\n",
       "      <td>1.2.4</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pixel Draw - Number Art Coloring Book</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.3</td>\n",
       "      <td>6.875232</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>100000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design;Creativity</td>\n",
       "      <td>June 20, 2018</td>\n",
       "      <td>1.1</td>\n",
       "      <td>4.4 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Paper flowers instructions</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.4</td>\n",
       "      <td>5.123964</td>\n",
       "      <td>5600.0</td>\n",
       "      <td>50000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>March 26, 2017</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.3 and up</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 App        Category  Rating  \\\n",
       "0     Photo Editor & Candy Camera & Grid & ScrapBook  ART_AND_DESIGN     4.1   \n",
       "1                                Coloring book moana  ART_AND_DESIGN     3.9   \n",
       "2  U Launcher Lite – FREE Live Cool Themes, Hide ...  ART_AND_DESIGN     4.7   \n",
       "4              Pixel Draw - Number Art Coloring Book  ART_AND_DESIGN     4.3   \n",
       "5                         Paper flowers instructions  ART_AND_DESIGN     4.4   \n",
       "\n",
       "     Reviews     Size  Installs  Type  Price Content Rating  \\\n",
       "0   5.075174  19000.0     10000  Free      0       Everyone   \n",
       "1   6.875232  14000.0    500000  Free      0       Everyone   \n",
       "2  11.379520   8700.0   5000000  Free      0       Everyone   \n",
       "4   6.875232   2800.0    100000  Free      0       Everyone   \n",
       "5   5.123964   5600.0     50000  Free      0       Everyone   \n",
       "\n",
       "                      Genres      Last Updated Current Ver   Android Ver  \n",
       "0               Art & Design   January 7, 2018       1.0.0  4.0.3 and up  \n",
       "1  Art & Design;Pretend Play  January 15, 2018       2.0.0  4.0.3 and up  \n",
       "2               Art & Design    August 1, 2018       1.2.4  4.0.3 and up  \n",
       "4    Art & Design;Creativity     June 20, 2018         1.1    4.4 and up  \n",
       "5               Art & Design    March 26, 2017         1.0    2.3 and up  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "66cb9acf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 8.2 Drop columns App, Last Updated, Current Ver, and Android Ver. These variables are not useful for our task.\n",
    "\n",
    "inp1.drop([\"Last Updated\",\"Current Ver\",\"Android Ver\",\"App\",\"Type\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "84713b9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Size</th>\n",
       "      <th>Installs</th>\n",
       "      <th>Price</th>\n",
       "      <th>Content Rating</th>\n",
       "      <th>Genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.1</td>\n",
       "      <td>5.075174</td>\n",
       "      <td>19000.0</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>3.9</td>\n",
       "      <td>6.875232</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>500000</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design;Pretend Play</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.7</td>\n",
       "      <td>11.379520</td>\n",
       "      <td>8700.0</td>\n",
       "      <td>5000000</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.3</td>\n",
       "      <td>6.875232</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design;Creativity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.4</td>\n",
       "      <td>5.123964</td>\n",
       "      <td>5600.0</td>\n",
       "      <td>50000</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Category  Rating    Reviews     Size  Installs  Price Content Rating  \\\n",
       "0  ART_AND_DESIGN     4.1   5.075174  19000.0     10000      0       Everyone   \n",
       "1  ART_AND_DESIGN     3.9   6.875232  14000.0    500000      0       Everyone   \n",
       "2  ART_AND_DESIGN     4.7  11.379520   8700.0   5000000      0       Everyone   \n",
       "4  ART_AND_DESIGN     4.3   6.875232   2800.0    100000      0       Everyone   \n",
       "5  ART_AND_DESIGN     4.4   5.123964   5600.0     50000      0       Everyone   \n",
       "\n",
       "                      Genres  \n",
       "0               Art & Design  \n",
       "1  Art & Design;Pretend Play  \n",
       "2               Art & Design  \n",
       "4    Art & Design;Creativity  \n",
       "5               Art & Design  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "ea543b28",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get dummy columns for Category, Genres, and Content Rating. This needs to be done as the models do not understand categorical data, and all data should be numeric. Dummy encoding is one way to convert character fields to numeric. Name of dataframe should be inp2\n",
    "\n",
    "inp2 = inp1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "51488f76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Size</th>\n",
       "      <th>Installs</th>\n",
       "      <th>Price</th>\n",
       "      <th>Content Rating</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Category_ART_AND_DESIGN</th>\n",
       "      <th>Category_AUTO_AND_VEHICLES</th>\n",
       "      <th>Category_BEAUTY</th>\n",
       "      <th>...</th>\n",
       "      <th>Category_PERSONALIZATION</th>\n",
       "      <th>Category_PHOTOGRAPHY</th>\n",
       "      <th>Category_PRODUCTIVITY</th>\n",
       "      <th>Category_SHOPPING</th>\n",
       "      <th>Category_SOCIAL</th>\n",
       "      <th>Category_SPORTS</th>\n",
       "      <th>Category_TOOLS</th>\n",
       "      <th>Category_TRAVEL_AND_LOCAL</th>\n",
       "      <th>Category_VIDEO_PLAYERS</th>\n",
       "      <th>Category_WEATHER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.1</td>\n",
       "      <td>5.075174</td>\n",
       "      <td>19000.0</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.9</td>\n",
       "      <td>6.875232</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>500000</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design;Pretend Play</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>11.379520</td>\n",
       "      <td>8700.0</td>\n",
       "      <td>5000000</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.3</td>\n",
       "      <td>6.875232</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design;Creativity</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.4</td>\n",
       "      <td>5.123964</td>\n",
       "      <td>5600.0</td>\n",
       "      <td>50000</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rating    Reviews     Size  Installs  Price Content Rating  \\\n",
       "0     4.1   5.075174  19000.0     10000      0       Everyone   \n",
       "1     3.9   6.875232  14000.0    500000      0       Everyone   \n",
       "2     4.7  11.379520   8700.0   5000000      0       Everyone   \n",
       "4     4.3   6.875232   2800.0    100000      0       Everyone   \n",
       "5     4.4   5.123964   5600.0     50000      0       Everyone   \n",
       "\n",
       "                      Genres  Category_ART_AND_DESIGN  \\\n",
       "0               Art & Design                        1   \n",
       "1  Art & Design;Pretend Play                        1   \n",
       "2               Art & Design                        1   \n",
       "4    Art & Design;Creativity                        1   \n",
       "5               Art & Design                        1   \n",
       "\n",
       "   Category_AUTO_AND_VEHICLES  Category_BEAUTY  ...  Category_PERSONALIZATION  \\\n",
       "0                           0                0  ...                         0   \n",
       "1                           0                0  ...                         0   \n",
       "2                           0                0  ...                         0   \n",
       "4                           0                0  ...                         0   \n",
       "5                           0                0  ...                         0   \n",
       "\n",
       "   Category_PHOTOGRAPHY  Category_PRODUCTIVITY  Category_SHOPPING  \\\n",
       "0                     0                      0                  0   \n",
       "1                     0                      0                  0   \n",
       "2                     0                      0                  0   \n",
       "4                     0                      0                  0   \n",
       "5                     0                      0                  0   \n",
       "\n",
       "   Category_SOCIAL  Category_SPORTS  Category_TOOLS  \\\n",
       "0                0                0               0   \n",
       "1                0                0               0   \n",
       "2                0                0               0   \n",
       "4                0                0               0   \n",
       "5                0                0               0   \n",
       "\n",
       "   Category_TRAVEL_AND_LOCAL  Category_VIDEO_PLAYERS  Category_WEATHER  \n",
       "0                          0                       0                 0  \n",
       "1                          0                       0                 0  \n",
       "2                          0                       0                 0  \n",
       "4                          0                       0                 0  \n",
       "5                          0                       0                 0  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's apply Dummy EnCoding on Column \"Category\"\n",
    "\n",
    "inp2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "b500ab74",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'Category'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-98-dd1213d7e895>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0minp2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCategory\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   5463\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5464\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5465\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5466\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5467\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'Category'"
     ]
    }
   ],
   "source": [
    "inp2.Category.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "b479877d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8496, 40)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "9d0cd181",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Art & Design', 'Art & Design;Pretend Play',\n",
       "       'Art & Design;Creativity', 'Auto & Vehicles', 'Beauty',\n",
       "       'Books & Reference', 'Business', 'Comics', 'Comics;Creativity',\n",
       "       'Communication', 'Dating', 'Education', 'Education;Creativity',\n",
       "       'Education;Education', 'Education;Music & Video',\n",
       "       'Education;Action & Adventure', 'Education;Pretend Play',\n",
       "       'Education;Brain Games', 'Entertainment',\n",
       "       'Entertainment;Brain Games', 'Entertainment;Creativity',\n",
       "       'Entertainment;Music & Video', 'Events', 'Finance', 'Food & Drink',\n",
       "       'Health & Fitness', 'House & Home', 'Libraries & Demo',\n",
       "       'Lifestyle', 'Lifestyle;Pretend Play', 'Card', 'Casual', 'Puzzle',\n",
       "       'Action', 'Arcade', 'Word', 'Racing', 'Casual;Creativity',\n",
       "       'Sports', 'Board', 'Simulation', 'Role Playing', 'Adventure',\n",
       "       'Strategy', 'Simulation;Education', 'Action;Action & Adventure',\n",
       "       'Trivia', 'Casual;Brain Games', 'Simulation;Action & Adventure',\n",
       "       'Educational;Creativity', 'Puzzle;Brain Games',\n",
       "       'Educational;Education', 'Card;Brain Games',\n",
       "       'Educational;Brain Games', 'Educational;Pretend Play',\n",
       "       'Casual;Action & Adventure', 'Entertainment;Education',\n",
       "       'Casual;Education', 'Casual;Pretend Play', 'Music;Music & Video',\n",
       "       'Racing;Action & Adventure', 'Arcade;Pretend Play',\n",
       "       'Adventure;Action & Adventure', 'Role Playing;Action & Adventure',\n",
       "       'Simulation;Pretend Play', 'Puzzle;Creativity',\n",
       "       'Sports;Action & Adventure', 'Educational;Action & Adventure',\n",
       "       'Arcade;Action & Adventure', 'Entertainment;Action & Adventure',\n",
       "       'Puzzle;Action & Adventure', 'Strategy;Action & Adventure',\n",
       "       'Music & Audio;Music & Video', 'Health & Fitness;Education',\n",
       "       'Adventure;Education', 'Board;Brain Games',\n",
       "       'Board;Action & Adventure', 'Board;Pretend Play',\n",
       "       'Casual;Music & Video', 'Role Playing;Pretend Play',\n",
       "       'Entertainment;Pretend Play', 'Video Players & Editors;Creativity',\n",
       "       'Card;Action & Adventure', 'Medical', 'Social', 'Shopping',\n",
       "       'Photography', 'Travel & Local',\n",
       "       'Travel & Local;Action & Adventure', 'Tools', 'Tools;Education',\n",
       "       'Personalization', 'Productivity', 'Parenting',\n",
       "       'Parenting;Music & Video', 'Parenting;Brain Games',\n",
       "       'Parenting;Education', 'Weather', 'Video Players & Editors',\n",
       "       'Video Players & Editors;Music & Video', 'News & Magazines',\n",
       "       'Maps & Navigation', 'Health & Fitness;Action & Adventure',\n",
       "       'Music', 'Educational', 'Casino', 'Adventure;Brain Games',\n",
       "       'Lifestyle;Education', 'Books & Reference;Education',\n",
       "       'Puzzle;Education', 'Role Playing;Brain Games',\n",
       "       'Strategy;Education', 'Racing;Pretend Play',\n",
       "       'Communication;Creativity', 'Strategy;Creativity'], dtype=object)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's apply Dummy EnCoding on Column \"Genres\"\n",
    "#get unique values in Column \"Genres\"\n",
    "\n",
    "inp2[\"Genres\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "92d3f155",
   "metadata": {},
   "outputs": [],
   "source": [
    "lists = []\n",
    "for i in inp2.Genres.value_counts().index:\n",
    "    if inp2.Genres.value_counts()[i]<20:\n",
    "        lists.append(i)\n",
    "inp2.Genres = ['Other' if i in lists else i for i in inp2.Genres] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "e36bf6b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Art & Design', 'Other', 'Auto & Vehicles', 'Beauty',\n",
       "       'Books & Reference', 'Business', 'Comics', 'Communication',\n",
       "       'Dating', 'Education', 'Education;Education',\n",
       "       'Education;Pretend Play', 'Entertainment',\n",
       "       'Entertainment;Music & Video', 'Events', 'Finance', 'Food & Drink',\n",
       "       'Health & Fitness', 'House & Home', 'Libraries & Demo',\n",
       "       'Lifestyle', 'Card', 'Casual', 'Puzzle', 'Action', 'Arcade',\n",
       "       'Word', 'Racing', 'Sports', 'Board', 'Simulation', 'Role Playing',\n",
       "       'Adventure', 'Strategy', 'Trivia', 'Educational;Education',\n",
       "       'Casual;Pretend Play', 'Medical', 'Social', 'Shopping',\n",
       "       'Photography', 'Travel & Local', 'Tools', 'Personalization',\n",
       "       'Productivity', 'Parenting', 'Weather', 'Video Players & Editors',\n",
       "       'News & Magazines', 'Maps & Navigation', 'Educational', 'Casino'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp2[\"Genres\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "1ea90f5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "inp2.Genres = pd.Categorical(inp2['Genres'])\n",
    "x = inp2[[\"Genres\"]]\n",
    "del inp2['Genres']\n",
    "dummies = pd.get_dummies(x, prefix = 'Genres')\n",
    "inp2 = pd.concat([inp2,dummies], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "4db7ce9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Size</th>\n",
       "      <th>Installs</th>\n",
       "      <th>Price</th>\n",
       "      <th>Content Rating</th>\n",
       "      <th>Category_ART_AND_DESIGN</th>\n",
       "      <th>Category_AUTO_AND_VEHICLES</th>\n",
       "      <th>Category_BEAUTY</th>\n",
       "      <th>Category_BOOKS_AND_REFERENCE</th>\n",
       "      <th>...</th>\n",
       "      <th>Genres_Simulation</th>\n",
       "      <th>Genres_Social</th>\n",
       "      <th>Genres_Sports</th>\n",
       "      <th>Genres_Strategy</th>\n",
       "      <th>Genres_Tools</th>\n",
       "      <th>Genres_Travel &amp; Local</th>\n",
       "      <th>Genres_Trivia</th>\n",
       "      <th>Genres_Video Players &amp; Editors</th>\n",
       "      <th>Genres_Weather</th>\n",
       "      <th>Genres_Word</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.1</td>\n",
       "      <td>5.075174</td>\n",
       "      <td>19000.0</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.9</td>\n",
       "      <td>6.875232</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>500000</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>11.379520</td>\n",
       "      <td>8700.0</td>\n",
       "      <td>5000000</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.3</td>\n",
       "      <td>6.875232</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.4</td>\n",
       "      <td>5.123964</td>\n",
       "      <td>5600.0</td>\n",
       "      <td>50000</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 91 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rating    Reviews     Size  Installs  Price Content Rating  \\\n",
       "0     4.1   5.075174  19000.0     10000      0       Everyone   \n",
       "1     3.9   6.875232  14000.0    500000      0       Everyone   \n",
       "2     4.7  11.379520   8700.0   5000000      0       Everyone   \n",
       "4     4.3   6.875232   2800.0    100000      0       Everyone   \n",
       "5     4.4   5.123964   5600.0     50000      0       Everyone   \n",
       "\n",
       "   Category_ART_AND_DESIGN  Category_AUTO_AND_VEHICLES  Category_BEAUTY  \\\n",
       "0                        1                           0                0   \n",
       "1                        1                           0                0   \n",
       "2                        1                           0                0   \n",
       "4                        1                           0                0   \n",
       "5                        1                           0                0   \n",
       "\n",
       "   Category_BOOKS_AND_REFERENCE  ...  Genres_Simulation  Genres_Social  \\\n",
       "0                             0  ...                  0              0   \n",
       "1                             0  ...                  0              0   \n",
       "2                             0  ...                  0              0   \n",
       "4                             0  ...                  0              0   \n",
       "5                             0  ...                  0              0   \n",
       "\n",
       "   Genres_Sports  Genres_Strategy  Genres_Tools  Genres_Travel & Local  \\\n",
       "0              0                0             0                      0   \n",
       "1              0                0             0                      0   \n",
       "2              0                0             0                      0   \n",
       "4              0                0             0                      0   \n",
       "5              0                0             0                      0   \n",
       "\n",
       "   Genres_Trivia  Genres_Video Players & Editors  Genres_Weather  Genres_Word  \n",
       "0              0                               0               0            0  \n",
       "1              0                               0               0            0  \n",
       "2              0                               0               0            0  \n",
       "4              0                               0               0            0  \n",
       "5              0                               0               0            0  \n",
       "\n",
       "[5 rows x 91 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "57086c6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8496, 91)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "3bfc25fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Everyone', 'Teen', 'Everyone 10+', 'Mature 17+',\n",
       "       'Adults only 18+', 'Unrated'], dtype=object)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's apply Dummy EnCoding on Column \"Content Rating\"\n",
    "\n",
    "inp2[\"Content Rating\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "27000851",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Size</th>\n",
       "      <th>Installs</th>\n",
       "      <th>Price</th>\n",
       "      <th>Category_ART_AND_DESIGN</th>\n",
       "      <th>Category_AUTO_AND_VEHICLES</th>\n",
       "      <th>Category_BEAUTY</th>\n",
       "      <th>Category_BOOKS_AND_REFERENCE</th>\n",
       "      <th>Category_BUSINESS</th>\n",
       "      <th>...</th>\n",
       "      <th>Genres_Trivia</th>\n",
       "      <th>Genres_Video Players &amp; Editors</th>\n",
       "      <th>Genres_Weather</th>\n",
       "      <th>Genres_Word</th>\n",
       "      <th>Content Rating_Adults only 18+</th>\n",
       "      <th>Content Rating_Everyone</th>\n",
       "      <th>Content Rating_Everyone 10+</th>\n",
       "      <th>Content Rating_Mature 17+</th>\n",
       "      <th>Content Rating_Teen</th>\n",
       "      <th>Content Rating_Unrated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.1</td>\n",
       "      <td>5.075174</td>\n",
       "      <td>19000.0</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.9</td>\n",
       "      <td>6.875232</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>500000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>11.379520</td>\n",
       "      <td>8700.0</td>\n",
       "      <td>5000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.3</td>\n",
       "      <td>6.875232</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.4</td>\n",
       "      <td>5.123964</td>\n",
       "      <td>5600.0</td>\n",
       "      <td>50000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 96 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rating    Reviews     Size  Installs  Price  Category_ART_AND_DESIGN  \\\n",
       "0     4.1   5.075174  19000.0     10000      0                        1   \n",
       "1     3.9   6.875232  14000.0    500000      0                        1   \n",
       "2     4.7  11.379520   8700.0   5000000      0                        1   \n",
       "4     4.3   6.875232   2800.0    100000      0                        1   \n",
       "5     4.4   5.123964   5600.0     50000      0                        1   \n",
       "\n",
       "   Category_AUTO_AND_VEHICLES  Category_BEAUTY  Category_BOOKS_AND_REFERENCE  \\\n",
       "0                           0                0                             0   \n",
       "1                           0                0                             0   \n",
       "2                           0                0                             0   \n",
       "4                           0                0                             0   \n",
       "5                           0                0                             0   \n",
       "\n",
       "   Category_BUSINESS  ...  Genres_Trivia  Genres_Video Players & Editors  \\\n",
       "0                  0  ...              0                               0   \n",
       "1                  0  ...              0                               0   \n",
       "2                  0  ...              0                               0   \n",
       "4                  0  ...              0                               0   \n",
       "5                  0  ...              0                               0   \n",
       "\n",
       "   Genres_Weather  Genres_Word  Content Rating_Adults only 18+  \\\n",
       "0               0            0                               0   \n",
       "1               0            0                               0   \n",
       "2               0            0                               0   \n",
       "4               0            0                               0   \n",
       "5               0            0                               0   \n",
       "\n",
       "   Content Rating_Everyone  Content Rating_Everyone 10+  \\\n",
       "0                        1                            0   \n",
       "1                        1                            0   \n",
       "2                        1                            0   \n",
       "4                        1                            0   \n",
       "5                        1                            0   \n",
       "\n",
       "   Content Rating_Mature 17+  Content Rating_Teen  Content Rating_Unrated  \n",
       "0                          0                    0                       0  \n",
       "1                          0                    0                       0  \n",
       "2                          0                    0                       0  \n",
       "4                          0                    0                       0  \n",
       "5                          0                    0                       0  \n",
       "\n",
       "[5 rows x 96 columns]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp2['Content Rating'] = pd.Categorical(inp2['Content Rating'])\n",
    "\n",
    "x = inp2[['Content Rating']]\n",
    "del inp2['Content Rating']\n",
    "\n",
    "dummies = pd.get_dummies(x, prefix = 'Content Rating')\n",
    "inp2 = pd.concat([inp2,dummies], axis=1)\n",
    "inp2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "5e0b2c34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8496, 96)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "0ec0ae19",
   "metadata": {},
   "outputs": [],
   "source": [
    "#9. Train test split  and apply 70-30 split. Name the new dataframes df_train and df_test.\n",
    " #Separate the dataframes into X_train, y_train, X_test, and y_test.\n",
    "\n",
    "from sklearn.model_selection import train_test_split as tts\n",
    "from sklearn.linear_model import LinearRegression as LR\n",
    "from sklearn.metrics import mean_squared_error as mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "6cd4b1c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "d1 = inp2\n",
    "X = d1.drop('Rating',axis=1)\n",
    "y = d1['Rating']\n",
    "\n",
    "Xtrain, Xtest, ytrain, ytest = tts(X,y, test_size=0.3, random_state=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "2552f463",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#11. Model building - Use linear regression as the technique\n",
    "\n",
    "reg_all = LR()\n",
    "reg_all.fit(Xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "73fe3370",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The R2 value of the Training Set is : 0.074\n"
     ]
    }
   ],
   "source": [
    "#11.2 Report the R2 on the train set\n",
    "\n",
    "R2_train = round(reg_all.score(Xtrain,ytrain),3)\n",
    "print(\"The R2 value of the Training Set is : {}\".format(R2_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "07fe0a61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The R2 value of the Testing Set is : 0.063\n"
     ]
    }
   ],
   "source": [
    "#12. Make predictions on test set and report R2.\n",
    "\n",
    "R2_test = round(reg_all.score(Xtest,ytest),3)\n",
    "print(\"The R2 value of the Testing Set is : {}\".format(R2_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c485c8d6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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