Shreyansh_Tiwari_CSE_assignment3.ipynb

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        "Name: Shreyansh Tiwari"
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        "Logistic Regression"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import sklearn.metrics as metrics\n",
        "from sklearn.preprocessing import LabelEncoder"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data=pd.read_csv(\"heart.csv\")\n",
        "data.describe()"
      ],
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              "              Age   RestingBP  Cholesterol   FastingBS       MaxHR  \\\n",
              "count  918.000000  918.000000   918.000000  918.000000  918.000000   \n",
              "mean    53.510893  132.396514   198.799564    0.233115  136.809368   \n",
              "std      9.432617   18.514154   109.384145    0.423046   25.460334   \n",
              "min     28.000000    0.000000     0.000000    0.000000   60.000000   \n",
              "25%     47.000000  120.000000   173.250000    0.000000  120.000000   \n",
              "50%     54.000000  130.000000   223.000000    0.000000  138.000000   \n",
              "75%     60.000000  140.000000   267.000000    0.000000  156.000000   \n",
              "max     77.000000  200.000000   603.000000    1.000000  202.000000   \n",
              "\n",
              "          Oldpeak  HeartDisease  \n",
              "count  918.000000    918.000000  \n",
              "mean     0.887364      0.553377  \n",
              "std      1.066570      0.497414  \n",
              "min     -2.600000      0.000000  \n",
              "25%      0.000000      0.000000  \n",
              "50%      0.600000      1.000000  \n",
              "75%      1.500000      1.000000  \n",
              "max      6.200000      1.000000  "
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              "      <th>mean</th>\n",
              "      <td>53.510893</td>\n",
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              "      <th>min</th>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>47.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>173.250000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>54.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>223.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.600000</td>\n",
              "      <td>1.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>60.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>267.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>156.000000</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>1.000000</td>\n",
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              "      <th>max</th>\n",
              "      <td>77.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>603.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>6.200000</td>\n",
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          "data": {
            "text/plain": [
              "   Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  MaxHR  \\\n",
              "0   40   M           ATA        140          289          0     Normal    172   \n",
              "1   49   F           NAP        160          180          0     Normal    156   \n",
              "2   37   M           ATA        130          283          0         ST     98   \n",
              "3   48   F           ASY        138          214          0     Normal    108   \n",
              "4   54   M           NAP        150          195          0     Normal    122   \n",
              "\n",
              "  ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
              "0              N      0.0       Up             0  \n",
              "1              N      1.0     Flat             1  \n",
              "2              N      0.0       Up             0  \n",
              "3              Y      1.5     Flat             1  \n",
              "4              N      0.0       Up             0  "
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              "      <td>49</td>\n",
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              "      <td>NAP</td>\n",
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              "      <th>2</th>\n",
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              "      <td>M</td>\n",
              "      <td>ATA</td>\n",
              "      <td>130</td>\n",
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              "      <td>ST</td>\n",
              "      <td>98</td>\n",
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              "      <td>Up</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>48</td>\n",
              "      <td>F</td>\n",
              "      <td>ASY</td>\n",
              "      <td>138</td>\n",
              "      <td>214</td>\n",
              "      <td>0</td>\n",
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              "      <td>Flat</td>\n",
              "      <td>1</td>\n",
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              "      <th>4</th>\n",
              "      <td>54</td>\n",
              "      <td>M</td>\n",
              "      <td>NAP</td>\n",
              "      <td>150</td>\n",
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              "      <td>0</td>\n",
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        "le=LabelEncoder()\n",
        "data.ChestPainType = le.fit_transform(data.ChestPainType)\n",
        "data.RestingECG = le.fit_transform(data.RestingECG)\n",
        "data.ExerciseAngina = le.fit_transform(data.ExerciseAngina)\n",
        "data.ST_Slope = le.fit_transform(data.ST_Slope)\n",
        "data.Sex = le.fit_transform(data.Sex)\n",
        "data.head()"
      ],
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        "outputId": "1c7d3294-86d1-4217-f12b-b3e44b8e1669"
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      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Age  Sex  ChestPainType  RestingBP  Cholesterol  FastingBS  RestingECG  \\\n",
              "0   40    1              1        140          289          0           1   \n",
              "1   49    0              2        160          180          0           1   \n",
              "2   37    1              1        130          283          0           2   \n",
              "3   48    0              0        138          214          0           1   \n",
              "4   54    1              2        150          195          0           1   \n",
              "\n",
              "   MaxHR  ExerciseAngina  Oldpeak  ST_Slope  HeartDisease  \n",
              "0    172               0      0.0         2             0  \n",
              "1    156               0      1.0         1             1  \n",
              "2     98               0      0.0         2             0  \n",
              "3    108               1      1.5         1             1  \n",
              "4    122               0      0.0         2             0  "
            ],
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              "\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "Eqnd4T9DHj9R"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x=data.drop(\"HeartDisease\",axis=1)\n",
        "y=data[\"HeartDisease\"]"
      ],
      "metadata": {
        "id": "o6fQ_luukykR"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=20,stratify=y)"
      ],
      "metadata": {
        "id": "M4rz3jP7mj6z"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression"
      ],
      "metadata": {
        "id": "kOBh0HNSorHU"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr=LinearRegression()"
      ],
      "metadata": {
        "id": "fTPPEJ3ko4HY"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr.fit(x_train,y_train)\n",
        "y_pred=lr.predict(x_test)\n",
        "from sklearn.metrics import mean_squared_error\n",
        "mean_squared_error(y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dsOwo3S6o7RE",
        "outputId": "4073a5b1-7124-4644-f5a5-762a871a8370"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.13026512503639598"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "knn classification"
      ],
      "metadata": {
        "id": "EwqhljDIsg9l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "accuracy_rate=[]\n",
        "\n",
        "for i in range(1,40):\n",
        "    classifier= KNeighborsClassifier(n_neighbors =i)\n",
        "    score = cross_val_score(classifier,x,y,cv=10)\n",
        "    accuracy_rate.append(score.mean())"
      ],
      "metadata": {
        "id": "phiEruFLsd0U"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "error_rate = []\n",
        "\n",
        "for i in range(1,40):\n",
        "    \n",
        "    knn = KNeighborsClassifier(n_neighbors=i)\n",
        "    knn.fit(x_train,y_train)\n",
        "    pred_i = knn.predict(x_test)\n",
        "    error_rate.append(np.mean(pred_i != y_test))"
      ],
      "metadata": {
        "id": "rh-XLA27JSwk"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "plt.figure(figsize=(10,6))\n",
        "\n",
        "plt.plot(range(1,40),accuracy_rate,color='blue', linestyle='dashed', marker='o',\n",
        "         markerfacecolor='red', markersize=10)\n",
        "plt.title('Error Rate vs. K Value')\n",
        "plt.xlabel('K')\n",
        "plt.ylabel('Error Rate')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "hfuPklyzJgBm",
        "outputId": "cba70176-2135-4d9c-e414-c7f9f9ce6da4"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Error Rate')"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
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      "execution_count": 17,
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              "   Age  Sex  ChestPainType  RestingBP  Cholesterol  FastingBS  RestingECG  \\\n",
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          "metadata": {},
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    {
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        "inputs_n = data.drop(['Age','RestingBP','Cholesterol','FastingBS','MaxHR','HeartDisease','ExerciseAngina','Oldpeak','ST_Slope'],axis='columns')\n",
        "target=data['HeartDisease']"
      ],
      "metadata": {
        "id": "xhD2YPcgocga"
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      "execution_count": 18,
      "outputs": []
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              "     Sex  ChestPainType  RestingECG\n",
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              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "target"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uCQkIlK0FAtC",
        "outputId": "dc49d952-103d-442e-ae0e-b8fb2e808146"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      0\n",
              "1      1\n",
              "2      0\n",
              "3      1\n",
              "4      0\n",
              "      ..\n",
              "913    1\n",
              "914    1\n",
              "915    1\n",
              "916    1\n",
              "917    0\n",
              "Name: HeartDisease, Length: 918, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import tree\n",
        "model = tree.DecisionTreeClassifier()\n",
        "model.fit(inputs_n, target)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nfeQnC5iFU3A",
        "outputId": "d12f3afd-f61b-4ffc-b4ad-dcca472c7633"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(inputs_n,target)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dEgjuQy5FZ-t",
        "outputId": "def4886f-361f-42df-d2fd-bc42956e1746"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7788671023965141"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict([[1,1,1]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t3NMNsyIFgiE",
        "outputId": "1cb218d9-a1c6-4a46-ae8d-fa836337b7de"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "M2KntJYdFjpT"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Forest classification"
      ],
      "metadata": {
        "id": "M3uzE4DkI5u9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X=inputs_n\n",
        "y=target"
      ],
      "metadata": {
        "id": "mG1cmAELxHE5"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2)"
      ],
      "metadata": {
        "id": "U74jeOWkxSK-"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "model = RandomForestClassifier(n_estimators=20)\n",
        "model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o8DKPJYRxhTw",
        "outputId": "80875df1-449a-42b5-a798-ce4100e51256"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(n_estimators=20)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lDKjlw_pxkXO",
        "outputId": "d5a17e38-14fe-4f5a-b68a-05963f107752"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.782608695652174"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_predicted = model.predict(X_test)"
      ],
      "metadata": {
        "id": "hxeiwwiwxoD1"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test, y_predicted)\n",
        "cm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DuqazNEOy79Y",
        "outputId": "24ffe4fd-f39a-41f7-8438-b092fa71f894"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[70, 27],\n",
              "       [13, 74]])"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sn\n",
        "plt.figure(figsize=(10,7))\n",
        "sn.heatmap(cm, annot=True)\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Truth')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "id": "iQs1bqbmy_Kr",
        "outputId": "1ac3b8fc-60d1-47bc-b9d2-e720bd647fd5"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(69.0, 0.5, 'Truth')"
            ]
          },
          "metadata": {},
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ONyRdzaHzDNE"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}