Carseats(Lab and Q10).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "CarseatsLab.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOWSnZO2ZptekzQYIQL08ZE",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/PhantomBlade009/ISLR_LinReg_Solutions/blob/main/Carseats(Lab%20and%20Q10).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JWcsn40jGr7p"
      },
      "source": [
        "#Lab section here, question 10 solved below"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eKeLbIylyMoc"
      },
      "source": [
        "import pandas as pd \n",
        "import numpy as np  \n",
        "from scipy import stats"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vDvgC3OSnV90"
      },
      "source": [
        "df =pd.read_csv('https://raw.githubusercontent.com/mscaudill/IntroStatLearn/master/data/Carseats.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 219
        },
        "id": "CiZ1PICknf9Y",
        "outputId": "92f783b8-bd96-4f22-e857-8c086af38812"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Unnamed: 0</th>\n",
              "      <th>Sales</th>\n",
              "      <th>CompPrice</th>\n",
              "      <th>Income</th>\n",
              "      <th>Advertising</th>\n",
              "      <th>Population</th>\n",
              "      <th>Price</th>\n",
              "      <th>ShelveLoc</th>\n",
              "      <th>Age</th>\n",
              "      <th>Education</th>\n",
              "      <th>Urban</th>\n",
              "      <th>US</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>9.50</td>\n",
              "      <td>138</td>\n",
              "      <td>73</td>\n",
              "      <td>11</td>\n",
              "      <td>276</td>\n",
              "      <td>120</td>\n",
              "      <td>Bad</td>\n",
              "      <td>42</td>\n",
              "      <td>17</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>11.22</td>\n",
              "      <td>111</td>\n",
              "      <td>48</td>\n",
              "      <td>16</td>\n",
              "      <td>260</td>\n",
              "      <td>83</td>\n",
              "      <td>Good</td>\n",
              "      <td>65</td>\n",
              "      <td>10</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>10.06</td>\n",
              "      <td>113</td>\n",
              "      <td>35</td>\n",
              "      <td>10</td>\n",
              "      <td>269</td>\n",
              "      <td>80</td>\n",
              "      <td>Medium</td>\n",
              "      <td>59</td>\n",
              "      <td>12</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>7.40</td>\n",
              "      <td>117</td>\n",
              "      <td>100</td>\n",
              "      <td>4</td>\n",
              "      <td>466</td>\n",
              "      <td>97</td>\n",
              "      <td>Medium</td>\n",
              "      <td>55</td>\n",
              "      <td>14</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>4.15</td>\n",
              "      <td>141</td>\n",
              "      <td>64</td>\n",
              "      <td>3</td>\n",
              "      <td>340</td>\n",
              "      <td>128</td>\n",
              "      <td>Bad</td>\n",
              "      <td>38</td>\n",
              "      <td>13</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0  Sales  CompPrice  Income  ...  Age  Education  Urban   US\n",
              "0           1   9.50        138      73  ...   42         17    Yes  Yes\n",
              "1           2  11.22        111      48  ...   65         10    Yes  Yes\n",
              "2           3  10.06        113      35  ...   59         12    Yes  Yes\n",
              "3           4   7.40        117     100  ...   55         14    Yes  Yes\n",
              "4           5   4.15        141      64  ...   38         13    Yes   No\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 219
        },
        "id": "RUK84tbzni10",
        "outputId": "99b05f8d-984f-41f9-cb25-989453199751"
      },
      "source": [
        "df.drop(columns=df.columns[0], inplace=True)\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Sales</th>\n",
              "      <th>CompPrice</th>\n",
              "      <th>Income</th>\n",
              "      <th>Advertising</th>\n",
              "      <th>Population</th>\n",
              "      <th>Price</th>\n",
              "      <th>ShelveLoc</th>\n",
              "      <th>Age</th>\n",
              "      <th>Education</th>\n",
              "      <th>Urban</th>\n",
              "      <th>US</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9.50</td>\n",
              "      <td>138</td>\n",
              "      <td>73</td>\n",
              "      <td>11</td>\n",
              "      <td>276</td>\n",
              "      <td>120</td>\n",
              "      <td>Bad</td>\n",
              "      <td>42</td>\n",
              "      <td>17</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>11.22</td>\n",
              "      <td>111</td>\n",
              "      <td>48</td>\n",
              "      <td>16</td>\n",
              "      <td>260</td>\n",
              "      <td>83</td>\n",
              "      <td>Good</td>\n",
              "      <td>65</td>\n",
              "      <td>10</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10.06</td>\n",
              "      <td>113</td>\n",
              "      <td>35</td>\n",
              "      <td>10</td>\n",
              "      <td>269</td>\n",
              "      <td>80</td>\n",
              "      <td>Medium</td>\n",
              "      <td>59</td>\n",
              "      <td>12</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7.40</td>\n",
              "      <td>117</td>\n",
              "      <td>100</td>\n",
              "      <td>4</td>\n",
              "      <td>466</td>\n",
              "      <td>97</td>\n",
              "      <td>Medium</td>\n",
              "      <td>55</td>\n",
              "      <td>14</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.15</td>\n",
              "      <td>141</td>\n",
              "      <td>64</td>\n",
              "      <td>3</td>\n",
              "      <td>340</td>\n",
              "      <td>128</td>\n",
              "      <td>Bad</td>\n",
              "      <td>38</td>\n",
              "      <td>13</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Sales  CompPrice  Income  Advertising  ...  Age  Education Urban   US\n",
              "0   9.50        138      73           11  ...   42         17   Yes  Yes\n",
              "1  11.22        111      48           16  ...   65         10   Yes  Yes\n",
              "2  10.06        113      35           10  ...   59         12   Yes  Yes\n",
              "3   7.40        117     100            4  ...   55         14   Yes  Yes\n",
              "4   4.15        141      64            3  ...   38         13   Yes   No\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_ESdQJl0nuEg",
        "outputId": "4375102f-f167-42fa-c618-4868debfb9cc"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sales          0\n",
              "CompPrice      0\n",
              "Income         0\n",
              "Advertising    0\n",
              "Population     0\n",
              "Price          0\n",
              "ShelveLoc      0\n",
              "Age            0\n",
              "Education      0\n",
              "Urban          0\n",
              "US             0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p0dgkqoDoTyD"
      },
      "source": [
        "from sklearn.preprocessing import OneHotEncoder \n",
        "from sklearn.preprocessing import OrdinalEncoder  \n",
        "ohe = OneHotEncoder(sparse=False)\n",
        "oe = OrdinalEncoder()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ILiUbw4oqM6V"
      },
      "source": [
        "ShelveLoc_encoded = ohe.fit_transform(df['ShelveLoc'].values.reshape(-1,1))\n",
        "US_encoded = oe.fit_transform(df['US'].values.reshape(-1,1))\n",
        "Urban_encoded = oe.fit_transform(df['Urban'].values.reshape(-1,1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R-NKgYG7n_hF",
        "outputId": "546e2c32-c09b-466d-dd1c-96ad28b69c92"
      },
      "source": [
        "X = df.iloc[:, 1:df.shape[1]].drop(columns=['ShelveLoc', 'US', 'Urban']).values\n",
        "X = np.column_stack((X,ShelveLoc_encoded,US_encoded, Urban_encoded))\n",
        "y = df.iloc[:,0].values.reshape(-1,1)\n",
        "print(X.shape, y.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(400, 12) (400, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0txbdsevpDoS"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler \n",
        "scaler = StandardScaler() \n",
        "X_norm = scaler.fit_transform(X) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jGn8YE3JX0Y9"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_norm,y, test_size = 0.3, random_state=4)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ei2izdHgYIHc"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression \n",
        "lr = LinearRegression()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9MeQdxUvYS9O"
      },
      "source": [
        "lr.fit(X_train, y_train) \n",
        "y_predict = lr.predict(X_test) \n",
        "lr.fit(X_norm, y)\n",
        "y_predict_tot = lr.predict(X_norm)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0WWO6ssNYeIL",
        "outputId": "21705dc3-a6ab-4a85-8b94-c808a7f6bd35"
      },
      "source": [
        "from sklearn.metrics import mean_squared_error \n",
        "from sklearn.metrics import r2_score\n",
        "n= X.shape[0] \n",
        "p= X.shape[1]\n",
        "adjusted_r2 = 1 - (1- r2_score(y_test, y_predict))*(n-1)*(1/(n-p-1))\n",
        "print(\"RMSE Train = \", (mean_squared_error(y, y_predict_tot))**(1/2)) \n",
        "print(\"RMSE Test = \", (mean_squared_error(y_test, y_predict))**(1/2)) \n",
        "print(\"R2 Score = \", r2_score(y, y_predict_tot))  \n",
        "print(\"Adjusted R2 Score = \", adjusted_r2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSE Train =  1.0035356425291149\n",
            "RMSE Test =  1.01908769009286\n",
            "R2 Score =  0.8734133434127012\n",
            "Adjusted R2 Score =  0.8798566232675059\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gkERGshaYMXm"
      },
      "source": [
        "def summary(X, y, y_predict, lr):\n",
        "  from scipy import stats \n",
        "  n = X.shape[0] \n",
        "  p = X.shape[1] \n",
        "  R2 = r2_score(y,y_predict) \n",
        "  adjusted_r2 = 1- (1-R2)*((n-1)/(n-p-1)) \n",
        "  RMSE = mean_squared_error(y,y_predict)**(1/2) \n",
        "  print(\"RMSE = \", RMSE) \n",
        "  print(\"R2 Score = \", R2)\n",
        "  print(\"Adjusted R2 =\" ,adjusted_r2) \n",
        "\n",
        "  weights = np.append(lr.intercept_,lr.coef_)\n",
        "\n",
        "  #newX = pd.DataFrame({\"Constant\":np.ones(len(X))}).join(pd.DataFrame(X))\n",
        "  #MSE = (mean_squared_error(y, y_predict_tot))/(len(newX)-len(newX.columns))\n",
        "\n",
        "  # Note if you don't want to use a DataFrame replace the two lines above with\n",
        "  newX = np.append(np.ones((len(X),1)), X, axis=1)\n",
        "  MSE = (sum((y-y_predict)**2))/(newX.shape[0]-newX.shape[1])\n",
        "\n",
        "\n",
        "  var_b = MSE*(np.linalg.inv(np.dot(newX.T,newX)).diagonal())\n",
        "  sd_b = np.sqrt(var_b)\n",
        "  ts_b = weights/ sd_b\n",
        "\n",
        "  p_values =[2.0*(1.0-stats.t.cdf(np.abs(i),(newX.shape[0]-newX.shape[1]))) for i in ts_b]\n",
        "\n",
        "  sd_b = np.round(sd_b,3)\n",
        "  ts_b = np.round(ts_b,3)\n",
        "  p_values = np.round(p_values,100)\n",
        "  weights = np.round(weights,4)\n",
        "\n",
        "  myDF3 = pd.DataFrame()\n",
        "  myDF3[\"Coefficients\"],myDF3[\"Standard Errors\"],myDF3[\"t values\"],myDF3[\"Probabilities\"] = [weights,sd_b,ts_b,p_values]\n",
        "  print(myDF3)\n",
        "\n",
        "  interval_95 = pd.DataFrame(np.column_stack((weights-2*sd_b, weights+2*sd_b)), columns=['LowerBound', 'UpperBound'])\n",
        "  print('95 per intervals')\n",
        "  print(interval_95)\n",
        "  return"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Arf0w8ZZ1Bs",
        "outputId": "ac4a9f95-6224-42f9-b5f5-d5fc0b028ded"
      },
      "source": [
        "summary(X,y,y_predict_tot,lr)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSE =  1.0035356425291149\n",
            "R2 Score =  0.8734133434127012\n",
            "Adjusted R2 = 0.8694881757665833\n",
            "    Coefficients  Standard Errors  t values  Probabilities\n",
            "0         7.4963              NaN       NaN            NaN\n",
            "1         1.4215            0.004   342.281       0.000000\n",
            "2         0.4417            0.002   239.083       0.000000\n",
            "3         0.8176            0.011    73.406       0.000000\n",
            "4         0.0306            0.000    82.489       0.000000\n",
            "5        -2.2549            0.003  -843.115       0.000000\n",
            "6        -0.7450            0.003  -233.852       0.000000\n",
            "7        -0.0552            0.020    -2.797       0.005416\n",
            "8        -0.9267              NaN       NaN            NaN\n",
            "9         1.0965              NaN       NaN            NaN\n",
            "10       -0.1060              NaN       NaN            NaN\n",
            "11       -0.0881            0.150    -0.587       0.557457\n",
            "12        0.0560            0.113     0.495       0.620594\n",
            "95 per intervals\n",
            "    LowerBound  UpperBound\n",
            "0          NaN         NaN\n",
            "1       1.4135      1.4295\n",
            "2       0.4377      0.4457\n",
            "3       0.7956      0.8396\n",
            "4       0.0306      0.0306\n",
            "5      -2.2609     -2.2489\n",
            "6      -0.7510     -0.7390\n",
            "7      -0.0952     -0.0152\n",
            "8          NaN         NaN\n",
            "9          NaN         NaN\n",
            "10         NaN         NaN\n",
            "11     -0.3881      0.2119\n",
            "12     -0.1700      0.2820\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:23: RuntimeWarning: invalid value encountered in sqrt\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "2DdxY7eZYp3w",
        "outputId": "48b3f76a-f7f9-47d5-a673-6a2478ffd911"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.scatter(y_predict_tot, y_predict_tot-y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fe600151490>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j7ttPRzyGoBz"
      },
      "source": [
        "#Questions from here:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RCdVPOCA8d5P",
        "outputId": "71371327-99c1-4c5d-dfe6-e2e45d402b11"
      },
      "source": [
        "X = np.column_stack((df['Price'].values, US_encoded, Urban_encoded))\n",
        "X = X.astype(np.float) \n",
        "y = df['Sales'].values.reshape(-1,1)\n",
        "print(X.shape, y.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(400, 3) (400, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EkKUvhcEblme"
      },
      "source": [
        "lr.fit(X,y)\n",
        "y_predict = lr.predict(X) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wKI5N8npjU0r",
        "outputId": "cf2cf31e-403c-4345-80bf-277b33587165"
      },
      "source": [
        "summary(X,y,y_predict,lr)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSE =  2.4600989164099034\n",
            "R2 Score =  0.23927539218405547\n",
            "Adjusted R2 = 0.23351232697332858\n",
            "   Coefficients  Standard Errors  t values  Probabilities\n",
            "0       13.0435            0.651    20.036       0.000000\n",
            "1       -0.0545            0.005   -10.389       0.000000\n",
            "2        1.2006            0.259     4.635       0.000005\n",
            "3       -0.0219            0.272    -0.081       0.935739\n",
            "95 per intervals\n",
            "   LowerBound  UpperBound\n",
            "0     11.7415     14.3455\n",
            "1     -0.0645     -0.0445\n",
            "2      0.6826      1.7186\n",
            "3     -0.5659      0.5221\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8m0DTZg7M7-k",
        "outputId": "76ac9f9a-cc6f-4bff-89a4-2d93c7ff81c8"
      },
      "source": [
        "#Rejecting feature Urban \n",
        "X = X = np.column_stack((df['Price'].values, US_encoded)) \n",
        "X.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(400, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7kX5voCwN1AL"
      },
      "source": [
        "lr.fit(X,y) \n",
        "y_predict = lr.predict(X) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "IGd77zG-dXTQ",
        "outputId": "b1aaf008-00fe-493c-faac-89bcf9a3571d"
      },
      "source": [
        "plt.scatter(y_predict, y_predict-y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f9c6a214390>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Y3PQQVyZOqb",
        "outputId": "0064cbe2-ad87-49ec-f191-22ce9cc344df"
      },
      "source": [
        "summary(X,y, y_predict, lr)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSE =  2.460119134208168\n",
            "R2 Score =  0.23926288842678567\n",
            "Adjusted R2 = 0.23543045965311715\n",
            "   Coefficients  Standard Errors  t values  Probabilities\n",
            "0       13.0308            0.631    20.652       0.000000\n",
            "1       -0.0545            0.005   -10.416       0.000000\n",
            "2        1.1996            0.258     4.641       0.000005\n",
            "95 per intervals\n",
            "   LowerBound  UpperBound\n",
            "0     11.7688     14.2928\n",
            "1     -0.0645     -0.0445\n",
            "2      0.6836      1.7156\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nCk5GmukG53G"
      },
      "source": [
        "#Q10 solutions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "irBaDz2hG8xl"
      },
      "source": [
        "b)Price has negatice relation with target, US (yes) has positive relation, Urban is statistically insignificant \n",
        "\n",
        "c) Sales = 13.04 -0.05Price  -0.02Urban(Yes =1) + 1.20US(Yes =1)\n",
        "\n",
        "d) We cannot reject null hypothesis for Urban\n",
        "\n",
        "f) There is not much difference in model a and e\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OicXN10oZh55"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}