Corners_vs_Goals.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "0a198bce",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "307c4164",
   "metadata": {},
   "source": [
    "## Step 1 - load in your data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "1507b250",
   "metadata": {},
   "outputs": [],
   "source": [
    "df= pd.read_csv('results.csv', encoding='latin-1') #https://www.kaggle.com/datasets/irkaal/english-premier-league-results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1c19e132",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Season</th>\n",
       "      <th>DateTime</th>\n",
       "      <th>HomeTeam</th>\n",
       "      <th>AwayTeam</th>\n",
       "      <th>FTHG</th>\n",
       "      <th>FTAG</th>\n",
       "      <th>FTR</th>\n",
       "      <th>HTHG</th>\n",
       "      <th>HTAG</th>\n",
       "      <th>HTR</th>\n",
       "      <th>...</th>\n",
       "      <th>HST</th>\n",
       "      <th>AST</th>\n",
       "      <th>HC</th>\n",
       "      <th>AC</th>\n",
       "      <th>HF</th>\n",
       "      <th>AF</th>\n",
       "      <th>HY</th>\n",
       "      <th>AY</th>\n",
       "      <th>HR</th>\n",
       "      <th>AR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1993-94</td>\n",
       "      <td>1993-08-14T00:00:00Z</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>Coventry</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1993-94</td>\n",
       "      <td>1993-08-14T00:00:00Z</td>\n",
       "      <td>Aston Villa</td>\n",
       "      <td>QPR</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>H</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1993-94</td>\n",
       "      <td>1993-08-14T00:00:00Z</td>\n",
       "      <td>Chelsea</td>\n",
       "      <td>Blackburn</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1993-94</td>\n",
       "      <td>1993-08-14T00:00:00Z</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>Sheffield Weds</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>H</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1993-94</td>\n",
       "      <td>1993-08-14T00:00:00Z</td>\n",
       "      <td>Man City</td>\n",
       "      <td>Leeds</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11108</th>\n",
       "      <td>2021-22</td>\n",
       "      <td>2022-04-09T17:30:00Z</td>\n",
       "      <td>Aston Villa</td>\n",
       "      <td>Tottenham</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11109</th>\n",
       "      <td>2021-22</td>\n",
       "      <td>2022-04-10T14:00:00Z</td>\n",
       "      <td>Brentford</td>\n",
       "      <td>West Ham</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>H</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>D</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11110</th>\n",
       "      <td>2021-22</td>\n",
       "      <td>2022-04-10T14:00:00Z</td>\n",
       "      <td>Leicester</td>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>H</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>H</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111</th>\n",
       "      <td>2021-22</td>\n",
       "      <td>2022-04-10T14:00:00Z</td>\n",
       "      <td>Norwich</td>\n",
       "      <td>Burnley</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>H</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>H</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11112</th>\n",
       "      <td>2021-22</td>\n",
       "      <td>2022-04-10T16:30:00Z</td>\n",
       "      <td>Man City</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>D</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>H</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11113 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Season              DateTime     HomeTeam        AwayTeam  FTHG  FTAG  \\\n",
       "0      1993-94  1993-08-14T00:00:00Z      Arsenal        Coventry     0     3   \n",
       "1      1993-94  1993-08-14T00:00:00Z  Aston Villa             QPR     4     1   \n",
       "2      1993-94  1993-08-14T00:00:00Z      Chelsea       Blackburn     1     2   \n",
       "3      1993-94  1993-08-14T00:00:00Z    Liverpool  Sheffield Weds     2     0   \n",
       "4      1993-94  1993-08-14T00:00:00Z     Man City           Leeds     1     1   \n",
       "...        ...                   ...          ...             ...   ...   ...   \n",
       "11108  2021-22  2022-04-09T17:30:00Z  Aston Villa       Tottenham     0     4   \n",
       "11109  2021-22  2022-04-10T14:00:00Z    Brentford        West Ham     2     0   \n",
       "11110  2021-22  2022-04-10T14:00:00Z    Leicester  Crystal Palace     2     1   \n",
       "11111  2021-22  2022-04-10T14:00:00Z      Norwich         Burnley     2     0   \n",
       "11112  2021-22  2022-04-10T16:30:00Z     Man City       Liverpool     2     2   \n",
       "\n",
       "      FTR  HTHG  HTAG  HTR  ...  HST  AST   HC   AC    HF    AF   HY   AY  \\\n",
       "0       A   NaN   NaN  NaN  ...  NaN  NaN  NaN  NaN   NaN   NaN  NaN  NaN   \n",
       "1       H   NaN   NaN  NaN  ...  NaN  NaN  NaN  NaN   NaN   NaN  NaN  NaN   \n",
       "2       A   NaN   NaN  NaN  ...  NaN  NaN  NaN  NaN   NaN   NaN  NaN  NaN   \n",
       "3       H   NaN   NaN  NaN  ...  NaN  NaN  NaN  NaN   NaN   NaN  NaN  NaN   \n",
       "4       D   NaN   NaN  NaN  ...  NaN  NaN  NaN  NaN   NaN   NaN  NaN  NaN   \n",
       "...    ..   ...   ...  ...  ...  ...  ...  ...  ...   ...   ...  ...  ...   \n",
       "11108   A   0.0   1.0    A  ...  8.0  5.0  9.0  3.0  12.0  14.0  2.0  3.0   \n",
       "11109   H   0.0   0.0    D  ...  7.0  1.0  4.0  6.0   2.0   6.0  0.0  1.0   \n",
       "11110   H   2.0   0.0    H  ...  3.0  3.0  3.0  4.0  11.0  12.0  1.0  1.0   \n",
       "11111   H   1.0   0.0    H  ...  6.0  4.0  6.0  7.0  12.0  10.0  1.0  1.0   \n",
       "11112   D   2.0   1.0    H  ...  5.0  4.0  4.0  1.0   9.0  11.0  1.0  4.0   \n",
       "\n",
       "        HR   AR  \n",
       "0      NaN  NaN  \n",
       "1      NaN  NaN  \n",
       "2      NaN  NaN  \n",
       "3      NaN  NaN  \n",
       "4      NaN  NaN  \n",
       "...    ...  ...  \n",
       "11108  0.0  0.0  \n",
       "11109  0.0  0.0  \n",
       "11110  0.0  0.0  \n",
       "11111  0.0  0.0  \n",
       "11112  0.0  0.0  \n",
       "\n",
       "[11113 rows x 23 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "ce18b823",
   "metadata": {},
   "outputs": [],
   "source": [
    "df =  df[df.Season == \"2021-22\"]\n",
    "df = df[df.FTR == 'H']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0c73391",
   "metadata": {},
   "source": [
    "## Step 2 - visualise data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "8bb9a651",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Home Team Goals Scored')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (12,8))\n",
    "\n",
    "sns.scatterplot(data = df, x = \"HC\", y = 'FTHG')\n",
    "\n",
    "ax.set_xlabel(\"Home Team Corners\")\n",
    "ax.set_ylabel(\"Home Team Goals Scored\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "a3905df5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1dbfa601e20>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#seaborn has a nice inbuilt function to see a linear regression line of best fit\n",
    "plot = sns.lmplot(data=df, x = \"HC\", y = 'FTHG', x_jitter=0.3, y_jitter=0.3, scatter_kws={\"color\": \"#fe4a49\", 'alpha': 0.6}, line_kws={\"color\": \"#2ab7ca\" , 'alpha': 0.2})\n",
    "plot.set_axis_labels(\"Home Corners\", \"Home Goals\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "475905dc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c2c9dfb1",
   "metadata": {},
   "source": [
    "## Step 3 - build linear regression model (sci-kit learn regression)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "df59cc7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "a1662cdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[['HC']] \n",
    "y = df['FTHG']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "db2c0c63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b7e4756",
   "metadata": {},
   "source": [
    "## Step 4 - use the linear regression model to make predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b91679b",
   "metadata": {},
   "source": [
    "#### this is c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "a524ffca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.301503538697368"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.intercept_ "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1961ea98",
   "metadata": {},
   "source": [
    "#### this is m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "c0ab6772",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.02980986])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.coef_ "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f646abb9",
   "metadata": {},
   "source": [
    "## Step 5 - assess how good the model is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "e279b593",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.005027717843691715"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#R2\n",
    "\n",
    "lr.score(X, y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "ac45912f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actual Values</th>\n",
       "      <th>Predicted Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10804</th>\n",
       "      <td>2</td>\n",
       "      <td>2.361123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10805</th>\n",
       "      <td>5</td>\n",
       "      <td>2.450553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10807</th>\n",
       "      <td>3</td>\n",
       "      <td>2.450553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10808</th>\n",
       "      <td>3</td>\n",
       "      <td>2.480363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10809</th>\n",
       "      <td>1</td>\n",
       "      <td>2.450553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11103</th>\n",
       "      <td>1</td>\n",
       "      <td>2.331313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11104</th>\n",
       "      <td>1</td>\n",
       "      <td>2.450553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11109</th>\n",
       "      <td>2</td>\n",
       "      <td>2.420743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11110</th>\n",
       "      <td>2</td>\n",
       "      <td>2.390933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111</th>\n",
       "      <td>2</td>\n",
       "      <td>2.480363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>130 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Actual Values  Predicted Values\n",
       "10804              2          2.361123\n",
       "10805              5          2.450553\n",
       "10807              3          2.450553\n",
       "10808              3          2.480363\n",
       "10809              1          2.450553\n",
       "...              ...               ...\n",
       "11103              1          2.331313\n",
       "11104              1          2.450553\n",
       "11109              2          2.420743\n",
       "11110              2          2.390933\n",
       "11111              2          2.480363\n",
       "\n",
       "[130 rows x 2 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actual = df.FTHG # these are the actual movie popularities in the data set\n",
    "predicts = lr.predict(X) #these are predicted movie popularities based on the linear regression model we just built\n",
    "\n",
    "pd.DataFrame({'Actual Values': actual, 'Predicted Values': predicts})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "95824282",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.263033095775444\n"
     ]
    }
   ],
   "source": [
    "#RMSE\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import numpy as np\n",
    "\n",
    "rmse=np.sqrt(mean_squared_error(predicts,actual))\n",
    "print(rmse)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "4d9e9040",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9921634348821979\n"
     ]
    }
   ],
   "source": [
    "#mae\n",
    "\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "mae=mean_absolute_error(predicts,actual)\n",
    "print(mae)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "fd984924",
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "X = sm.add_constant(X) # adding intercept"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "4f850e99",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = sm.OLS(y, X, hasconst = True)\n",
    "res=model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "c44c77ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>FTHG</td>       <th>  R-squared:         </th> <td>   0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>  -0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>  0.6468</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 06 Sep 2023</td> <th>  Prob (F-statistic):</th>  <td> 0.423</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>00:23:44</td>     <th>  Log-Likelihood:    </th> <td> -214.82</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   130</td>      <th>  AIC:               </th> <td>   433.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   128</td>      <th>  BIC:               </th> <td>   439.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    2.3015</td> <td>    0.245</td> <td>    9.393</td> <td> 0.000</td> <td>    1.817</td> <td>    2.786</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>HC</th>    <td>    0.0298</td> <td>    0.037</td> <td>    0.804</td> <td> 0.423</td> <td>   -0.044</td> <td>    0.103</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>28.650</td> <th>  Durbin-Watson:     </th> <td>   2.140</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  41.908</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.122</td> <th>  Prob(JB):          </th> <td>7.94e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.644</td> <th>  Cond. No.          </th> <td>    14.8</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                   FTHG   R-squared:                       0.005\n",
       "Model:                            OLS   Adj. R-squared:                 -0.003\n",
       "Method:                 Least Squares   F-statistic:                    0.6468\n",
       "Date:                Wed, 06 Sep 2023   Prob (F-statistic):              0.423\n",
       "Time:                        00:23:44   Log-Likelihood:                -214.82\n",
       "No. Observations:                 130   AIC:                             433.6\n",
       "Df Residuals:                     128   BIC:                             439.4\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          2.3015      0.245      9.393      0.000       1.817       2.786\n",
       "HC             0.0298      0.037      0.804      0.423      -0.044       0.103\n",
       "==============================================================================\n",
       "Omnibus:                       28.650   Durbin-Watson:                   2.140\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               41.908\n",
       "Skew:                           1.122   Prob(JB):                     7.94e-10\n",
       "Kurtosis:                       4.644   Cond. No.                         14.8\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.summary()"
   ]
  }
 ],
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