10AUG_YBIF.ipynb

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      "name": "10AUG.ipynb",
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      "metadata": {
        "id": "Dua2snyeG3G1"
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      "source": [
        "import pandas as pd"
      ]
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    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/ybifoundation/Dataset/main/House%20Prices.csv')"
      ],
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        "id": "ymSGoMNkJ7aS"
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      "outputs": []
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 21613 entries, 0 to 21612\n",
            "Data columns (total 21 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   ID             21613 non-null  int64  \n",
            " 1   Date           21613 non-null  object \n",
            " 2   Price          21613 non-null  float64\n",
            " 3   Bedrooms       21613 non-null  int64  \n",
            " 4   Bathrooms      21613 non-null  float64\n",
            " 5   Sqft_living    21613 non-null  int64  \n",
            " 6   Sqft_lot       21613 non-null  int64  \n",
            " 7   Floors         21613 non-null  float64\n",
            " 8   Waterfront     21613 non-null  int64  \n",
            " 9   View           21613 non-null  int64  \n",
            " 10  Condition      21613 non-null  int64  \n",
            " 11  Grade          21613 non-null  int64  \n",
            " 12  Sqft_above     21613 non-null  int64  \n",
            " 13  Sqft_basement  21613 non-null  int64  \n",
            " 14  Yr_built       21613 non-null  int64  \n",
            " 15  Yr_renovated   21613 non-null  int64  \n",
            " 16  zipcode        21613 non-null  int64  \n",
            " 17  Lat            21613 non-null  float64\n",
            " 18  Long           21613 non-null  float64\n",
            " 19  Sqft_living15  21613 non-null  int64  \n",
            " 20  Sqft_lot15     21613 non-null  int64  \n",
            "dtypes: float64(5), int64(15), object(1)\n",
            "memory usage: 3.5+ MB\n"
          ]
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
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          "data": {
            "text/plain": [
              "                ID         Price      Bedrooms     Bathrooms   Sqft_living  \\\n",
              "count  21613.00000  2.161300e+04  21613.000000  21613.000000  21613.000000   \n",
              "mean   10807.00000  5.401822e+05      3.370842      2.114757   2079.899736   \n",
              "std     6239.28002  3.673622e+05      0.930062      0.770163    918.440897   \n",
              "min        1.00000  7.500000e+04      0.000000      0.000000    290.000000   \n",
              "25%     5404.00000  3.219500e+05      3.000000      1.750000   1427.000000   \n",
              "50%    10807.00000  4.500000e+05      3.000000      2.250000   1910.000000   \n",
              "75%    16210.00000  6.450000e+05      4.000000      2.500000   2550.000000   \n",
              "max    21613.00000  7.700000e+06     33.000000      8.000000  13540.000000   \n",
              "\n",
              "           Sqft_lot        Floors    Waterfront          View     Condition  \\\n",
              "count  2.161300e+04  21613.000000  21613.000000  21613.000000  21613.000000   \n",
              "mean   1.510697e+04      1.494309      0.007542      0.234303      3.409430   \n",
              "std    4.142051e+04      0.539989      0.086517      0.766318      0.650743   \n",
              "min    5.200000e+02      1.000000      0.000000      0.000000      1.000000   \n",
              "25%    5.040000e+03      1.000000      0.000000      0.000000      3.000000   \n",
              "50%    7.618000e+03      1.500000      0.000000      0.000000      3.000000   \n",
              "75%    1.068800e+04      2.000000      0.000000      0.000000      4.000000   \n",
              "max    1.651359e+06      3.500000      1.000000      4.000000      5.000000   \n",
              "\n",
              "              Grade    Sqft_above  Sqft_basement      Yr_built  Yr_renovated  \\\n",
              "count  21613.000000  21613.000000   21613.000000  21613.000000  21613.000000   \n",
              "mean       7.656873   1788.390691     291.509045   1971.005136     84.402258   \n",
              "std        1.175459    828.090978     442.575043     29.373411    401.679240   \n",
              "min        1.000000    290.000000       0.000000   1900.000000      0.000000   \n",
              "25%        7.000000   1190.000000       0.000000   1951.000000      0.000000   \n",
              "50%        7.000000   1560.000000       0.000000   1975.000000      0.000000   \n",
              "75%        8.000000   2210.000000     560.000000   1997.000000      0.000000   \n",
              "max       13.000000   9410.000000    4820.000000   2015.000000   2015.000000   \n",
              "\n",
              "            zipcode           Lat          Long  Sqft_living15     Sqft_lot15  \n",
              "count  21613.000000  21613.000000  21613.000000   21613.000000   21613.000000  \n",
              "mean   98077.939805     47.560053   -122.213896    1986.552492   12768.455652  \n",
              "std       53.505026      0.138564      0.140828     685.391304   27304.179631  \n",
              "min    98001.000000     47.155900   -122.519000     399.000000     651.000000  \n",
              "25%    98033.000000     47.471000   -122.328000    1490.000000    5100.000000  \n",
              "50%    98065.000000     47.571800   -122.230000    1840.000000    7620.000000  \n",
              "75%    98118.000000     47.678000   -122.125000    2360.000000   10083.000000  \n",
              "max    98199.000000     47.777600   -121.315000    6210.000000  871200.000000  "
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>Price</th>\n",
              "      <th>Bedrooms</th>\n",
              "      <th>Bathrooms</th>\n",
              "      <th>Sqft_living</th>\n",
              "      <th>Sqft_lot</th>\n",
              "      <th>Floors</th>\n",
              "      <th>Waterfront</th>\n",
              "      <th>View</th>\n",
              "      <th>Condition</th>\n",
              "      <th>Grade</th>\n",
              "      <th>Sqft_above</th>\n",
              "      <th>Sqft_basement</th>\n",
              "      <th>Yr_built</th>\n",
              "      <th>Yr_renovated</th>\n",
              "      <th>zipcode</th>\n",
              "      <th>Lat</th>\n",
              "      <th>Long</th>\n",
              "      <th>Sqft_living15</th>\n",
              "      <th>Sqft_lot15</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>21613.00000</td>\n",
              "      <td>2.161300e+04</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>2.161300e+04</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>10807.00000</td>\n",
              "      <td>5.401822e+05</td>\n",
              "      <td>3.370842</td>\n",
              "      <td>2.114757</td>\n",
              "      <td>2079.899736</td>\n",
              "      <td>1.510697e+04</td>\n",
              "      <td>1.494309</td>\n",
              "      <td>0.007542</td>\n",
              "      <td>0.234303</td>\n",
              "      <td>3.409430</td>\n",
              "      <td>7.656873</td>\n",
              "      <td>1788.390691</td>\n",
              "      <td>291.509045</td>\n",
              "      <td>1971.005136</td>\n",
              "      <td>84.402258</td>\n",
              "      <td>98077.939805</td>\n",
              "      <td>47.560053</td>\n",
              "      <td>-122.213896</td>\n",
              "      <td>1986.552492</td>\n",
              "      <td>12768.455652</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>6239.28002</td>\n",
              "      <td>3.673622e+05</td>\n",
              "      <td>0.930062</td>\n",
              "      <td>0.770163</td>\n",
              "      <td>918.440897</td>\n",
              "      <td>4.142051e+04</td>\n",
              "      <td>0.539989</td>\n",
              "      <td>0.086517</td>\n",
              "      <td>0.766318</td>\n",
              "      <td>0.650743</td>\n",
              "      <td>1.175459</td>\n",
              "      <td>828.090978</td>\n",
              "      <td>442.575043</td>\n",
              "      <td>29.373411</td>\n",
              "      <td>401.679240</td>\n",
              "      <td>53.505026</td>\n",
              "      <td>0.138564</td>\n",
              "      <td>0.140828</td>\n",
              "      <td>685.391304</td>\n",
              "      <td>27304.179631</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.00000</td>\n",
              "      <td>7.500000e+04</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>290.000000</td>\n",
              "      <td>5.200000e+02</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>1900.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>98001.000000</td>\n",
              "      <td>47.155900</td>\n",
              "      <td>-122.519000</td>\n",
              "      <td>399.000000</td>\n",
              "      <td>651.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5404.00000</td>\n",
              "      <td>3.219500e+05</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.750000</td>\n",
              "      <td>1427.000000</td>\n",
              "      <td>5.040000e+03</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>1190.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1951.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>98033.000000</td>\n",
              "      <td>47.471000</td>\n",
              "      <td>-122.328000</td>\n",
              "      <td>1490.000000</td>\n",
              "      <td>5100.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>10807.00000</td>\n",
              "      <td>4.500000e+05</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>1910.000000</td>\n",
              "      <td>7.618000e+03</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>1560.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1975.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>98065.000000</td>\n",
              "      <td>47.571800</td>\n",
              "      <td>-122.230000</td>\n",
              "      <td>1840.000000</td>\n",
              "      <td>7620.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>16210.00000</td>\n",
              "      <td>6.450000e+05</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>2550.000000</td>\n",
              "      <td>1.068800e+04</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>2210.000000</td>\n",
              "      <td>560.000000</td>\n",
              "      <td>1997.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>98118.000000</td>\n",
              "      <td>47.678000</td>\n",
              "      <td>-122.125000</td>\n",
              "      <td>2360.000000</td>\n",
              "      <td>10083.000000</td>\n",
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              "      <th>max</th>\n",
              "      <td>21613.00000</td>\n",
              "      <td>7.700000e+06</td>\n",
              "      <td>33.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>13540.000000</td>\n",
              "      <td>1.651359e+06</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>9410.000000</td>\n",
              "      <td>4820.000000</td>\n",
              "      <td>2015.000000</td>\n",
              "      <td>2015.000000</td>\n",
              "      <td>98199.000000</td>\n",
              "      <td>47.777600</td>\n",
              "      <td>-121.315000</td>\n",
              "      <td>6210.000000</td>\n",
              "      <td>871200.000000</td>\n",
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      "execution_count": 35,
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          "data": {
            "text/plain": [
              "   ID             Date     Price  Bedrooms  Bathrooms  Sqft_living  Sqft_lot  \\\n",
              "0   1  20140916T000000  280000.0         6       3.00         2400      9373   \n",
              "1   2  20150422T000000  300000.0         6       3.00         2400      9373   \n",
              "2   3  20140508T000000  647500.0         4       1.75         2060     26036   \n",
              "3   4  20140811T000000  400000.0         3       1.00         1460     43000   \n",
              "4   5  20150401T000000  235000.0         3       1.00         1430      7599   \n",
              "\n",
              "   Floors  Waterfront  View  ...  Grade  Sqft_above  Sqft_basement  Yr_built  \\\n",
              "0     2.0           0     0  ...      7        2400              0      1991   \n",
              "1     2.0           0     0  ...      7        2400              0      1991   \n",
              "2     1.0           0     0  ...      8        1160            900      1947   \n",
              "3     1.0           0     0  ...      7        1460              0      1952   \n",
              "4     1.5           0     0  ...      6        1010            420      1930   \n",
              "\n",
              "   Yr_renovated  zipcode      Lat     Long  Sqft_living15  Sqft_lot15  \n",
              "0             0    98002  47.3262 -122.214           2060        7316  \n",
              "1             0    98002  47.3262 -122.214           2060        7316  \n",
              "2             0    98166  47.4444 -122.351           2590       21891  \n",
              "3             0    98166  47.4434 -122.347           2250       20023  \n",
              "4             0    98168  47.4783 -122.265           1290       10320  \n",
              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oUj0dimqKFR3",
        "outputId": "c938e5bf-3124-403b-9369-b51e74f51633"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['ID', 'Date', 'Price', 'Bedrooms', 'Bathrooms', 'Sqft_living',\n",
              "       'Sqft_lot', 'Floors', 'Waterfront', 'View', 'Condition', 'Grade',\n",
              "       'Sqft_above', 'Sqft_basement', 'Yr_built', 'Yr_renovated', 'zipcode',\n",
              "       'Lat', 'Long', 'Sqft_living15', 'Sqft_lot15'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df[['Price', 'Bedrooms', 'Bathrooms', 'Sqft_living',\n",
        "       'Sqft_lot', 'Floors', 'Waterfront', 'View', 'Condition', 'Grade',\n",
        "       'Sqft_above', 'Sqft_basement', 'Yr_built', 'Yr_renovated', 'zipcode',\n",
        "       'Lat', 'Long', 'Sqft_living15', 'Sqft_lot15']]\n",
        "y = df['Price']"
      ],
      "metadata": {
        "id": "YPzsCL3QHwiy"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "almmfbeOLx1d",
        "outputId": "2370bfc7-21c0-4f96-9300-7f1065a57e46"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(21613, 19)"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PjlIqvPQLy_o",
        "outputId": "cf3a3b7a-6397-4412-b97e-22cfbd38f3ac"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(21613,)"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "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,train_size=0.7,random_state=2529)"
      ],
      "metadata": {
        "id": "CuTLEjCfH0TE"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "model = LinearRegression()"
      ],
      "metadata": {
        "id": "DDTeZidjIV_y"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gW3A3MKzIfPq",
        "outputId": "c1d28fd3-368d-46e2-e489-18bff092d306"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.intercept_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m0UbCN-ILVZf",
        "outputId": "b58669af-45a7-4319-d78d-2754fb3cad4e"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-1.1641532182693481e-10"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.coef_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u2xYlX2iLYgX",
        "outputId": "583ea0df-ade0-47f5-8ff2-a3d65fa472c1"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1.00000000e+00, -8.49922139e-14, -6.06711625e-13,  3.05308205e-16,\n",
              "       -1.64200237e-17,  1.05244618e-13, -7.68348324e-12, -1.93679993e-13,\n",
              "       -1.05891381e-12, -1.20267961e-12,  2.49988773e-16,  6.31453731e-17,\n",
              "        1.07953701e-14, -1.42575235e-15,  7.85805667e-15, -8.23352325e-12,\n",
              "        3.17336041e-12, -1.25137370e-15,  7.11349584e-18])"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test)"
      ],
      "metadata": {
        "id": "VKl1J__5Ikrb"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_absolute_error,  r2_score\n",
        "mean_absolute_error(y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2NhhdG59Ish3",
        "outputId": "28103a79-9a36-42a2-ff38-815fa61abe64"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0503233076612719e-10"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "r2_score(y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UxPqQ5z29a5Y",
        "outputId": "62efd037-0d27-4d8e-a442-7fe23a77d83a"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Classification Problem"
      ],
      "metadata": {
        "id": "HUD2TUqZSrrD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "0YTqF2O_SqtO"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('https://github.com/ybifoundation/Dataset/raw/main/Diabetes.csv')"
      ],
      "metadata": {
        "id": "lqU64YxOSzbj"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ICnVmZ9tS7uC",
        "outputId": "5755baa1-2a4d-41bf-b9ca-a62d1ca6496d"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 768 entries, 0 to 767\n",
            "Data columns (total 9 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   pregnancies  768 non-null    int64  \n",
            " 1   glucose      768 non-null    int64  \n",
            " 2   diastolic    768 non-null    int64  \n",
            " 3   triceps      768 non-null    int64  \n",
            " 4   insulin      768 non-null    int64  \n",
            " 5   bmi          768 non-null    float64\n",
            " 6   dpf          768 non-null    float64\n",
            " 7   age          768 non-null    int64  \n",
            " 8   diabetes     768 non-null    int64  \n",
            "dtypes: float64(2), int64(7)\n",
            "memory usage: 54.1 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "nNqav8R3-aPR",
        "outputId": "5f727784-1434-4678-df53-03b46f00ac6a"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   pregnancies  glucose  diastolic  triceps  insulin   bmi    dpf  age  \\\n",
              "0            6      148         72       35        0  33.6  0.627   50   \n",
              "1            1       85         66       29        0  26.6  0.351   31   \n",
              "2            8      183         64        0        0  23.3  0.672   32   \n",
              "3            1       89         66       23       94  28.1  0.167   21   \n",
              "4            0      137         40       35      168  43.1  2.288   33   \n",
              "\n",
              "   diabetes  \n",
              "0         1  \n",
              "1         0  \n",
              "2         1  \n",
              "3         0  \n",
              "4         1  "
            ],
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df[['pregnancies','glucose','diastolic','insulin','bmi','dpf','age']]\n",
        "y = df['diabetes']"
      ],
      "metadata": {
        "id": "uTIqkVOqS9gZ"
      },
      "execution_count": 61,
      "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,train_size=0.7,random_state=2529)"
      ],
      "metadata": {
        "id": "f-ZLNZnZTOV9"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "model = LogisticRegression(max_iter=200)"
      ],
      "metadata": {
        "id": "pagsAGW_TcPN"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t6tCFz05TkkQ",
        "outputId": "13f523cf-d9a5-4c16-ea78-d16c1e3dad87"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(max_iter=200)"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test)\n",
        "print(y_pred)"
      ],
      "metadata": {
        "id": "9SPHkk33TxST",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d0fabcf7-4d95-4ff1-bfa3-73e7d61e47d7"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 0 1 0 0 1 1 1 0 1 0 1 0 0 0 1 1 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0\n",
            " 1 0 1 0 0 0 1 0 1 1 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 1 0 1 0 0 0\n",
            " 0 0 0 0 0 0 0 0 1 1 0 0 1 0 1 0 1 0 1 0 0 0 0 0 0 0 1 0 0 1 1 0 0 1 0 1 0\n",
            " 0 0 1 1 0 0 0 0 0 0 1 0 1 0 0 1 0 1 0 0 1 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0\n",
            " 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0\n",
            " 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0\n",
            " 0 1 0 0 0 0 0 0 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "accuracy_score(y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sSKnJHobL2ko",
        "outputId": "730b8268-08d3-4ac6-8a3f-15e1ccc61497"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7705627705627706"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Housing Price Prediction"
      ],
      "metadata": {
        "id": "LO2lLcM_-uYZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "ImjA4LVl_B0I"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('https://github.com/ybifoundation/Dataset/raw/main/Boston.csv')"
      ],
      "metadata": {
        "id": "gyDa21s0_DB_"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "9cfAEF16_Gpd",
        "outputId": "cc1c56fa-6484-4b4b-e4e3-2d777b977d5e"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      CRIM    ZN  INDUS  CHAS     NX     RM   AGE     DIS  RAD    TAX  \\\n",
              "0  0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296.0   \n",
              "1  0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242.0   \n",
              "2  0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242.0   \n",
              "3  0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222.0   \n",
              "4  0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222.0   \n",
              "\n",
              "   PTRATIO       B  LSTAT  MEDV  \n",
              "0     15.3  396.90   4.98  24.0  \n",
              "1     17.8  396.90   9.14  21.6  \n",
              "2     17.8  392.83   4.03  34.7  \n",
              "3     18.7  394.63   2.94  33.4  \n",
              "4     18.7  396.90   5.33  36.2  "
            ],
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-feb6632a-3564-4c13-80af-9249d520c295 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-feb6632a-3564-4c13-80af-9249d520c295');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          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": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# checking notnull values\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oDh1pjvS_HmP",
        "outputId": "a69bc9ba-5e84-45a5-db72-5349c5d7e021"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 506 entries, 0 to 505\n",
            "Data columns (total 14 columns):\n",
            " #   Column   Non-Null Count  Dtype  \n",
            "---  ------   --------------  -----  \n",
            " 0   CRIM     506 non-null    float64\n",
            " 1   ZN       506 non-null    float64\n",
            " 2   INDUS    506 non-null    float64\n",
            " 3   CHAS     506 non-null    int64  \n",
            " 4   NX       506 non-null    float64\n",
            " 5   RM       506 non-null    float64\n",
            " 6   AGE      506 non-null    float64\n",
            " 7   DIS      506 non-null    float64\n",
            " 8   RAD      506 non-null    int64  \n",
            " 9   TAX      506 non-null    float64\n",
            " 10  PTRATIO  506 non-null    float64\n",
            " 11  B        506 non-null    float64\n",
            " 12  LSTAT    506 non-null    float64\n",
            " 13  MEDV     506 non-null    float64\n",
            "dtypes: float64(12), int64(2)\n",
            "memory usage: 55.5 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bt0C68sk_I7j",
        "outputId": "f296b089-b4cc-4574-c230-fcb935f6782b"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX',\n",
              "       'PTRATIO', 'B', 'LSTAT', 'MEDV'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = df['MEDV']\n",
        "X = df[['CRIM', 'ZN', 'INDUS', 'CHAS', 'NX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX',\n",
        "       'PTRATIO', 'B', 'LSTAT']]"
      ],
      "metadata": {
        "id": "QETQ2mnN_Jzb"
      },
      "execution_count": 73,
      "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,train_size=0.7,random_state=2529)"
      ],
      "metadata": {
        "id": "zhf2kXTd_ayX"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "model = LinearRegression()"
      ],
      "metadata": {
        "id": "nkVqN-SF_rLp"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eeKnMXihAHVG",
        "outputId": "5044871b-d404-4bcc-f452-37297f05f695"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.intercept_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vMY7eXadAL9f",
        "outputId": "474a2aee-71e3-43a8-a25a-29a41c29dd10"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "34.21916368863014"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.coef_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6BBcSHK8APCY",
        "outputId": "b67a10df-c247-424c-a5a3-cc7b2f68d94f"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-1.29412069e-01,  3.65184937e-02,  1.54418944e-02,  2.35486887e+00,\n",
              "       -2.04171489e+01,  4.41356565e+00,  4.61075512e-03, -1.58626723e+00,\n",
              "        2.51478665e-01, -9.59591213e-03, -9.64169204e-01,  1.00972679e-02,\n",
              "       -5.43198745e-01])"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test)"
      ],
      "metadata": {
        "id": "AnYbgYXeAP-Z"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_absolute_error, r2_score\n",
        "mean_absolute_error(y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QOI1LL3cAdBN",
        "outputId": "55bd0afc-e5c2-49ba-c70f-052554a3f5c6"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.1550309276025073"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "r2_score(y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qShjPVuJAyhb",
        "outputId": "d3022cbe-f89a-40c7-b407-e77ebcf96523"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6551914852365506"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    }
  ]
}