diff --git a/Projects-ML/Reg-models/Project-2_Advertising.ipynb b/Projects-ML/Reg-models/Project-2_Advertising.ipynb
index 95b39db..79b9640 100644
--- a/Projects-ML/Reg-models/Project-2_Advertising.ipynb
+++ b/Projects-ML/Reg-models/Project-2_Advertising.ipynb
@@ -1648,11 +1648,818 @@
    ]
   },
   {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## K-Fold Cross-Validation\n",
-    "\n"
+    "K-fold cross-validation is a robust validation approach that can be adopted to verify if the model is\n",
+    "overfitting. The model, which generalizes well and does not overfit, should not be very sensitive to any\n",
+    "change in underlying training samples. K-fold cross-validation can do this by building and validating\n",
+    "multiple models by resampling multiple training and validation sets from the original dataset.\n",
+    "\n",
+    "1. Split the training data set into K subsets of equal size. Each subset will be called a fold. Let the\n",
+    "folds be labelled as $f_1, f_2, … , f_K$. Generally, the value of $K$ is taken to be 5 or 10.\n",
+    "2. For i = 1 to K\n",
+    "    (a) Fold $f_i$ is used as validation set and all the remaining K – 1 folds as training set.\n",
+    "    (b) Train the model using the training set and calculate the accuracy of the model in fold $f_i$.\n",
+    "\n",
+    "Calculate the final accuracy by averaging the accuracies in the test data across all K models. The average accuracy value shows how the model will behave in the real world. The variance of these accuracies is an indication of the robustness of the model.\n",
+    "\n",
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ipl_auction_df = pd.read_csv('IPL-IMB381IPL2013.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 130 entries, 0 to 129\n",
+      "Data columns (total 26 columns):\n",
+      " #   Column         Non-Null Count  Dtype  \n",
+      "---  ------         --------------  -----  \n",
+      " 0   Sl.NO.         130 non-null    int64  \n",
+      " 1   PLAYER NAME    130 non-null    object \n",
+      " 2   AGE            130 non-null    int64  \n",
+      " 3   COUNTRY        130 non-null    object \n",
+      " 4   TEAM           130 non-null    object \n",
+      " 5   PLAYING ROLE   130 non-null    object \n",
+      " 6   T-RUNS         130 non-null    int64  \n",
+      " 7   T-WKTS         130 non-null    int64  \n",
+      " 8   ODI-RUNS-S     130 non-null    int64  \n",
+      " 9   ODI-SR-B       130 non-null    float64\n",
+      " 10  ODI-WKTS       130 non-null    int64  \n",
+      " 11  ODI-SR-BL      130 non-null    float64\n",
+      " 12  CAPTAINCY EXP  130 non-null    int64  \n",
+      " 13  RUNS-S         130 non-null    int64  \n",
+      " 14  HS             130 non-null    int64  \n",
+      " 15  AVE            130 non-null    float64\n",
+      " 16  SR-B           130 non-null    float64\n",
+      " 17  SIXERS         130 non-null    int64  \n",
+      " 18  RUNS-C         130 non-null    int64  \n",
+      " 19  WKTS           130 non-null    int64  \n",
+      " 20  AVE-BL         130 non-null    float64\n",
+      " 21  ECON           130 non-null    float64\n",
+      " 22  SR-BL          130 non-null    float64\n",
+      " 23  AUCTION YEAR   130 non-null    int64  \n",
+      " 24  BASE PRICE     130 non-null    int64  \n",
+      " 25  SOLD PRICE     130 non-null    int64  \n",
+      "dtypes: float64(7), int64(15), object(4)\n",
+      "memory usage: 26.5+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "ipl_auction_df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_features = [\"AGE\", \"COUNTRY\", \"PLAYING ROLE\", \"T-RUNS\", \"T-WKTS\", \"ODI-RUNS-S\", \"ODI-SR-B\", \n",
+    "              \"ODI-WKTS\", \"ODI-SR-BL\", \"CAPTAINCY EXP\", \"RUNS-S\", \"HS\", \"AVE\", \"SR-B\", \"SIXERS\",\n",
+    "              \"RUNS-C\", \"WKTS\", \"AVE-BL\", \"ECON\", \"SR-BL\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Out of these, there are four categorical features that need to be encoded into dummy features using\n",
+    "OHE (One Hot Encoding)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize a list with the categorical feature names.\n",
+    "categorical_features = [\"AGE\", \"COUNTRY\", \"PLAYING ROLE\", \"CAPTAINCY EXP\"]\n",
+    "#get_dummies() is invoked to return the dummy features.\n",
+    "ipl_auction_encoded_df = pd.get_dummies( ipl_auction_df[X_features], columns = categorical_features, \n",
+    "                                        drop_first = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['T-RUNS', 'T-WKTS', 'ODI-RUNS-S', 'ODI-SR-B', 'ODI-WKTS', 'ODI-SR-BL',\n",
+       "       'RUNS-S', 'HS', 'AVE', 'SR-B', 'SIXERS', 'RUNS-C', 'WKTS', 'AVE-BL',\n",
+       "       'ECON', 'SR-BL', 'AGE_2', 'AGE_3', 'COUNTRY_BAN', 'COUNTRY_ENG',\n",
+       "       'COUNTRY_IND', 'COUNTRY_NZ', 'COUNTRY_PAK', 'COUNTRY_SA', 'COUNTRY_SL',\n",
+       "       'COUNTRY_WI', 'COUNTRY_ZIM', 'PLAYING ROLE_Batsman',\n",
+       "       'PLAYING ROLE_Bowler', 'PLAYING ROLE_W. Keeper', 'CAPTAINCY EXP_1'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 157,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ipl_auction_encoded_df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = ipl_auction_encoded_df\n",
+    "y = ipl_auction_df['SOLD PRICE']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Standardization of X and Y\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "## Initializing the StandardScaler\n",
+    "scaler = StandardScaler()\n",
+    "## Standardize all the feature columns\n",
+    "X_scaled = scaler.fit_transform(X)\n",
+    "## Standardizing Y explictly by subtracting mean and dividing by standard deviation\n",
+    "y = (y - y.mean()) / y.std()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Split the Dataset into Train and Test\n",
+    "X_train, X_test, y_train, y_test = train_test_split( X_scaled, y, test_size=0.2, random_state = 42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 169,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Building the model\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "linreg = LinearRegression()\n",
+    "linreg.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-0.43539611, -0.04632556,  0.50840867, -0.03323988,  0.2220377 ,\n",
+       "       -0.05065703,  0.17282657, -0.49173336,  0.58571405, -0.11654753,\n",
+       "        0.24880095,  0.09546057,  0.16428731,  0.26400753, -0.08253341,\n",
+       "       -0.28643889, -0.26842214, -0.21910913, -0.02622351,  0.24817898,\n",
+       "        0.18760332,  0.10776084,  0.04737488,  0.05191335,  0.01235245,\n",
+       "        0.00547115, -0.03124706,  0.08530192,  0.01790803, -0.05077454,\n",
+       "        0.18745577])"
+      ]
+     },
+     "execution_count": 170,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "linreg.coef_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "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>columns</th>\n",
+       "      <th>coef</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>T-RUNS</td>\n",
+       "      <td>-0.435396</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>T-WKTS</td>\n",
+       "      <td>-0.046326</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>ODI-RUNS-S</td>\n",
+       "      <td>0.508409</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>ODI-SR-B</td>\n",
+       "      <td>-0.033240</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>ODI-WKTS</td>\n",
+       "      <td>0.222038</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      columns      coef\n",
+       "0      T-RUNS -0.435396\n",
+       "1      T-WKTS -0.046326\n",
+       "2  ODI-RUNS-S  0.508409\n",
+       "3    ODI-SR-B -0.033240\n",
+       "4    ODI-WKTS  0.222038"
+      ]
+     },
+     "execution_count": 171,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Saving the coefficeint as a dataframe\n",
+    "columns_coef_def = pd.DataFrame({'columns': ipl_auction_encoded_df.columns, 'coef' : linreg.coef_})\n",
+    "columns_coef_def.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## Sorting the features by coefficient values in descending order\n",
+    "sorted_coef_vals = columns_coef_def.sort_values( 'coef', ascending=False)\n",
+    "plt.figure(figsize=(12,8))\n",
+    "sns.barplot(x= 'coef', y=\"columns\", data = sorted_coef_vals)\n",
+    "plt.xlabel(\"Coefficients from Linear regression\")\n",
+    "plt.ylabel(\"Features\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Few observation from this figure:\n",
+    "- AVE, ODI-RUNS-S, SIXERS are top three highly influential features which determine the player’s SOLD PRICE\n",
+    "- Higher ECON, SR-B and AGE have negative effect on SOLD PRICE.\n",
+    "- Interestingly, higher test runs (T-Runs) and highest score (HS) have negative effect on the SOLD\n",
+    "PRICE. Note that few of these counter-intuitive sign for coefficients could be due to multicollinearity.\n",
+    "For example, we expect SR-B (batting strike rate) to have a positive effect on the\n",
+    "SOLD PRICE."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Calculating the RMSE:** We can calculate the RMSE on training and test sets to understand the model’s ability to predict SOLD PRICE."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import metrics\n",
+    "\n",
+    "# Takes a model as a parameter. Prints the RMSE on train and test set\n",
+    "def get_train_test_rmse( model ):\n",
+    "    # Predicting on training dataset\n",
+    "\n",
+    "    y_train_pred = model.predict( X_train )\n",
+    "    # Compare the actual y with predicted y in the training dataset\n",
+    "    rmse_train = round(np.sqrt(metrics.mean_squared_error( y_train, y_train_pred)),3)\n",
+    "\n",
+    "    # Predicting on test dataset\n",
+    "    y_test_pred = model.predict( X_test )\n",
+    "    # Compare the actual y with predicted y in the test dataset\n",
+    "    rmse_test = round(np.sqrt(metrics.mean_squared_error( y_test, y_test_pred)),3)\n",
+    "    print( \"train: \", rmse_train, \" test: \", rmse_test )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "train:  0.679  test:  0.749\n"
+     ]
+    }
+   ],
+   "source": [
+    "get_train_test_rmse(linreg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "RMSE on the training set is 0.679, while it is 0.749 on the test set. A good model that generalizes well\n",
+    "needs to have a very similar error on training and test sets. Large difference indicates that the model\n",
+    "may be overfitting to the training set. Most widely used approach to deal with model overfitting is called Regularization, which will be discussed in the next section."
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Applying regularization:** One way to deal with overfitting is regularization. It is observed that overfitting is typically caused by inflation of the coefficients. To avoid overfitting, the coefficients should be regulated by penalizing potential inflation of coefficients. Regularization applies penalties on parameters if they inflate to large values and keeps them from being weighted too heavily.\n",
+    "\n",
+    "The coefficients are penalized by adding the coefficient terms to the cost function. If the coefficients\n",
+    "become large, the cost increases significantly. So, the optimizer controls the coefficient values to minimize the cost function. Following are the two approaches that can be used for adding a penalty to the cost function:\n",
+    "1. **L1 Norm:** Summation of the absolute value of the coefficients. This is also called Least Absolute\n",
+    "Shrinkage and Selection Operator (**LASSO** Term) (Tibshirani, 1996). The corresponding cost function is given by: \n",
+    "$$\\epsilon_\\text{MSE} = \\frac{1}{n}\\sum_{i=1}^n (y_i - (\\beta_0+\\beta_1 X_1+ ... + \\beta_n X_n))^2 +\\alpha \\sum_{i=1}^n |\\beta_i|$$\n",
+    "\n",
+    "2. **L2 Norm:** Summation of the squared value of the coefficients. This is called **Ridge** Term (Hoerl A E and Kennard Kennard 1970). The cost function is given by:\n",
+    "$$\\epsilon_\\text{MSE} = \\frac{1}{n}\\sum_{i=1}^n (y_i - (\\beta_0+\\beta_1 X_1+ ... + \\beta_n X_n))^2 +\\alpha \\sum_{i=1}^n (\\beta_i)^2$$\n",
+    "\n",
+    "Ridge term distributes (smoothens) the coefficient values across all the features, whereas LASSO seems\n",
+    "to reduce some of the coefficients to zero. Features with coefficients value as zero can be treated as features with no contribution to the model. So, LASSO can also be used for feature selection, that is, remove features with zero coefficients, thereby reducing the number of features.\n",
+    "\n",
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1. **Ridge Regression:** `sklearn.linear_model` provides Ridge regression for building linear models by applying L2 penalty. Ridge regression takes the following parameters:\n",
+    "1. `alpha` $\\alpha$ – float – is the regularization strength; regularization strength must be a positive float. Regularization improves the estimation of the parameters and reduces the variance of the estimates. Larger values of alpha imply stronger regularization.\n",
+    "2. `max_iter` – int (integer) – is the maximum number of iterations for the gradient solver."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Ridge(alpha=1, max_iter=500)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Ridge</label><div class=\"sk-toggleable__content\"><pre>Ridge(alpha=1, max_iter=500)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "Ridge(alpha=1, max_iter=500)"
+      ]
+     },
+     "execution_count": 175,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Importing Ridge Regression\n",
+    "from sklearn.linear_model import Ridge\n",
+    "# Applying alpha = 1 and running the algorithms for maximum of 500 iterations\n",
+    "ridge = Ridge(alpha = 1, max_iter = 500)\n",
+    "ridge.fit( X_train, y_train )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "train:  0.68  test:  0.724\n"
+     ]
+    }
+   ],
+   "source": [
+    "get_train_test_rmse(ridge)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The difference in RMSE on train and test has reduced because of penalty effect. The difference can be\n",
+    "reduced by applying a stronger penalty. For example, apply a value as 2.0."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "train:  0.682  test:  0.706\n"
+     ]
+    }
+   ],
+   "source": [
+    "ridge = Ridge(alpha = 2.0, max_iter = 1000)\n",
+    "ridge.fit( X_train, y_train )\n",
+    "get_train_test_rmse( ridge )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The difference in model accuracy on training and test has reduced. We need to calculate the optimal\n",
+    "value for $\\alpha$. This can be achieved in many ways. Multiple values of $\\alpha$ can be tested before arriving at the optimal value. The parameters which can be tuned are called hyperparameters in machine learning. Here $\\alpha$ is a hyperparameter."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`sklearn.model_selection.GridSearchCV` can help search for the optimal value (will be discussed later). For now, let us assume the optimal value for a is 2.0."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "2. **LASSO Regression:** `sklearn.linear_model` provides LASSO regression for building linear models by applying L1 penalty. Two key parameters for LASSO regression are:\n",
+    "1. `alpha` – float – multiplies the L1 term. Default value is set to 1.0.\n",
+    "2. `max_iter` – int – Maximum number of iterations for gradient solver."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-5 {color: black;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Lasso(alpha=0.01, max_iter=500)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Lasso</label><div class=\"sk-toggleable__content\"><pre>Lasso(alpha=0.01, max_iter=500)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "Lasso(alpha=0.01, max_iter=500)"
+      ]
+     },
+     "execution_count": 178,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Importing LASSO Regression\n",
+    "from sklearn.linear_model import Lasso\n",
+    "\n",
+    "# Applying alpha = 1 and running the algorithms for maximum of 500 iterations\n",
+    "lasso = Lasso(alpha = 0.01, max_iter = 500)\n",
+    "lasso.fit( X_train, y_train )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "train:  0.688  test:  0.698\n"
+     ]
+    }
+   ],
+   "source": [
+    "get_train_test_rmse(lasso)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It can be noticed that the model is not overfitting and the difference between train RMSE and test RMSE\n",
+    "is very small. LASSO reduces some of the coefficient values to 0, which indicates that these features are not necessary for explaining the variance in the outcome variable."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will store the feature names, coefficient values in a DataFrame and then filter the features with\n",
+    "zero coefficients"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "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>columns</th>\n",
+       "      <th>coef</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>T-RUNS</td>\n",
+       "      <td>-0.301242</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>T-WKTS</td>\n",
+       "      <td>-0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>ODI-RUNS-S</td>\n",
+       "      <td>0.413059</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>ODI-SR-B</td>\n",
+       "      <td>-0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>ODI-WKTS</td>\n",
+       "      <td>0.157779</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      columns      coef\n",
+       "0      T-RUNS -0.301242\n",
+       "1      T-WKTS -0.000000\n",
+       "2  ODI-RUNS-S  0.413059\n",
+       "3    ODI-SR-B -0.000000\n",
+       "4    ODI-WKTS  0.157779"
+      ]
+     },
+     "execution_count": 180,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "## Storing the feature names and coefficient values in the DataFrame\n",
+    "lasso_coef_df = pd.DataFrame( { 'columns': ipl_auction_encoded_df.columns, 'coef': lasso.coef_ } )\n",
+    "lasso_coef_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "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>columns</th>\n",
+       "      <th>coef</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>T-WKTS</td>\n",
+       "      <td>-0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>ODI-SR-B</td>\n",
+       "      <td>-0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>AVE-BL</td>\n",
+       "      <td>-0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>PLAYING ROLE_Bowler</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                columns  coef\n",
+       "1                T-WKTS  -0.0\n",
+       "3              ODI-SR-B  -0.0\n",
+       "13               AVE-BL  -0.0\n",
+       "28  PLAYING ROLE_Bowler   0.0"
+      ]
+     },
+     "execution_count": 181,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "## Filtering out coefficients with zeros\n",
+    "lasso_coef_df[lasso_coef_df.coef == 0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The LASSO regression indicates that the features listed under “columns” are not influencing factors for\n",
+    "predicting the SOLD PRICE as the respective coefficients are 0.0."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "3. **Elastic Net Regression:** ElasticNet regression combines both L1 and L2 regularizations to build a regression model. The corresponding cost function is given by:\n",
+    "\n",
+    "$$\\epsilon_\\text{MSE} = \\frac{1}{n}\\sum_{i=1}^n (y_i - (\\beta_0+\\beta_1 X_1+ ... + \\beta_n X_n))^2 +\\gamma \\sum_{i=1}^n |\\beta_i| +\\sigma \\sum_{i=1}^n (\\beta_i)^2$$\n",
+    "\n",
+    "While building ElasticNet regression model, both hyperparameters $\\sigma$ (L2) and $\\gamma$ (L1) need to be set. ElasticNet takes the following two parameters:\n",
+    "1. `alpha` - Constant that multiplies the penalty terms. Default value is set to 1.0. (alpha = $\\sigma +\\gamma$)\n",
+    "2. `l1_ratio`: The ElasticNet mixing parameter, with `0 <= l1_ratio <= 1`.\n",
+    "   \n",
+    "   $$l1\\_ratio = \\frac{\\gamma}{\\sigma+\\gamma}$$\n",
+    "\n",
+    "   where:\n",
+    "   - `l1_ration = 0` implies that the penalty is an L2 penalty\n",
+    "   - `l1_ratio = 1` implies that it is an L1 penalty.\n",
+    "   - `0 < l1_ratio < 1` implies that the penalty is a combination of L1 and L2."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Let's take a example:** penalties applied are $\\gamma =0.01$ and $\\sigma =1.0$. So \n",
+    "\n",
+    "alpha =$\\sigma+\\gamma =1.01$ and `l1_ratio` = $\\frac{\\gamma}{\\gamma+\\sigma} =0.0099$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "train:  0.789  test:  0.665\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import ElasticNet\n",
+    "enet = ElasticNet(alpha = 1.01, l1_ratio = 0.001, max_iter = 500)\n",
+    "enet.fit( X_train, y_train )\n",
+    "get_train_test_rmse( enet )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As we can see, applying both the regularizations did not improve the model performance. It has become\n",
+    "worse. In this case, we can choose to apply only L1 (LASSO) regularization, which seems to deal with the\n",
+    "overfitting problem efficiently."
    ]
   },
   {