ML_models_03.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c6f442f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import BaggingRegressor, VotingRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import sklearn.neighbors\n",
    "\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "74536d6d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df=pd.read_csv('https://raw.githubusercontent.com/zlatte1/ds22_project/main/data/mvoies_processed_noTitle.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ea717520",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1810 entries, 0 to 1809\n",
      "Data columns (total 50 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   Rating                  1810 non-null   float64\n",
      " 1   Year                    1810 non-null   int64  \n",
      " 2   Month                   1810 non-null   int64  \n",
      " 3   Runtime                 1810 non-null   int64  \n",
      " 4   cert_G                  1810 non-null   int64  \n",
      " 5   cert_NC-17              1810 non-null   int64  \n",
      " 6   cert_PG                 1810 non-null   int64  \n",
      " 7   cert_PG-13              1810 non-null   int64  \n",
      " 8   cert_R                  1810 non-null   int64  \n",
      " 9   cert_TV-14              1810 non-null   int64  \n",
      " 10  cert_TV-G               1810 non-null   int64  \n",
      " 11  cert_TV-MA              1810 non-null   int64  \n",
      " 12  cert_TV-PG              1810 non-null   int64  \n",
      " 13  cert_TV-Y7              1810 non-null   int64  \n",
      " 14  top_50_director_1       1810 non-null   int64  \n",
      " 15  top_50_director_2       1810 non-null   int64  \n",
      " 16  top_1000_Stars_1        1810 non-null   int64  \n",
      " 17  top_1000_Stars_2        1810 non-null   int64  \n",
      " 18  top_1000_Stars_3        1810 non-null   int64  \n",
      " 19  top_1000_Stars_4        1810 non-null   int64  \n",
      " 20  genre_Action            1810 non-null   int64  \n",
      " 21  genre_Adventure         1810 non-null   int64  \n",
      " 22  genre_Animation         1810 non-null   int64  \n",
      " 23  genre_Biography         1810 non-null   int64  \n",
      " 24  genre_Comedy            1810 non-null   int64  \n",
      " 25  genre_Crime             1810 non-null   int64  \n",
      " 26  genre_Drama             1810 non-null   int64  \n",
      " 27  genre_Family            1810 non-null   int64  \n",
      " 28  genre_Fantasy           1810 non-null   int64  \n",
      " 29  genre_History           1810 non-null   int64  \n",
      " 30  genre_Horror            1810 non-null   int64  \n",
      " 31  genre_Music             1810 non-null   int64  \n",
      " 32  genre_Musical           1810 non-null   int64  \n",
      " 33  genre_Mystery           1810 non-null   int64  \n",
      " 34  genre_Romance           1810 non-null   int64  \n",
      " 35  genre_Sci-Fi            1810 non-null   int64  \n",
      " 36  genre_Sport             1810 non-null   int64  \n",
      " 37  genre_Thriller          1810 non-null   int64  \n",
      " 38  genre_War               1810 non-null   int64  \n",
      " 39  genre_Western           1810 non-null   int64  \n",
      " 40  contient_Africa         1810 non-null   int64  \n",
      " 41  contient_Asia           1810 non-null   int64  \n",
      " 42  contient_Europe         1810 non-null   int64  \n",
      " 43  contient_North America  1810 non-null   int64  \n",
      " 44  contient_Oceania        1810 non-null   int64  \n",
      " 45  contient_South America  1810 non-null   int64  \n",
      " 46  Budget_inf              1810 non-null   float64\n",
      " 47  Income_inf              1810 non-null   float64\n",
      " 48  Profit_inf              1810 non-null   float64\n",
      " 49  ROI_inf                 1810 non-null   float64\n",
      "dtypes: float64(5), int64(45)\n",
      "memory usage: 707.2 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a4ffee49",
   "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>Rating</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Runtime</th>\n",
       "      <th>cert_G</th>\n",
       "      <th>cert_NC-17</th>\n",
       "      <th>cert_PG</th>\n",
       "      <th>cert_PG-13</th>\n",
       "      <th>cert_R</th>\n",
       "      <th>cert_TV-14</th>\n",
       "      <th>...</th>\n",
       "      <th>contient_Africa</th>\n",
       "      <th>contient_Asia</th>\n",
       "      <th>contient_Europe</th>\n",
       "      <th>contient_North America</th>\n",
       "      <th>contient_Oceania</th>\n",
       "      <th>contient_South America</th>\n",
       "      <th>Budget_inf</th>\n",
       "      <th>Income_inf</th>\n",
       "      <th>Profit_inf</th>\n",
       "      <th>ROI_inf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1810.000000</td>\n",
       "      <td>1810.000000</td>\n",
       "      <td>1810.000000</td>\n",
       "      <td>1810.000000</td>\n",
       "      <td>1810.000000</td>\n",
       "      <td>1810.000000</td>\n",
       "      <td>1810.000000</td>\n",
       "      <td>1810.000000</td>\n",
       "      <td>1810.000000</td>\n",
       "      <td>1810.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1810.000000</td>\n",
       "      <td>1810.000000</td>\n",
       "      <td>1810.000000</td>\n",
       "      <td>1810.000000</td>\n",
       "      <td>1810.000000</td>\n",
       "      <td>1810.000000</td>\n",
       "      <td>1.810000e+03</td>\n",
       "      <td>1.810000e+03</td>\n",
       "      <td>1.810000e+03</td>\n",
       "      <td>1810.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.693702</td>\n",
       "      <td>2012.021547</td>\n",
       "      <td>7.105525</td>\n",
       "      <td>113.859116</td>\n",
       "      <td>0.011602</td>\n",
       "      <td>0.003315</td>\n",
       "      <td>0.118785</td>\n",
       "      <td>0.385635</td>\n",
       "      <td>0.465746</td>\n",
       "      <td>0.002762</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008840</td>\n",
       "      <td>0.127624</td>\n",
       "      <td>0.365193</td>\n",
       "      <td>0.932597</td>\n",
       "      <td>0.046409</td>\n",
       "      <td>0.007735</td>\n",
       "      <td>7.149188e+07</td>\n",
       "      <td>2.378169e+08</td>\n",
       "      <td>1.663250e+08</td>\n",
       "      <td>-5.023364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.886415</td>\n",
       "      <td>5.630121</td>\n",
       "      <td>3.551473</td>\n",
       "      <td>19.378273</td>\n",
       "      <td>0.107116</td>\n",
       "      <td>0.057496</td>\n",
       "      <td>0.323624</td>\n",
       "      <td>0.486880</td>\n",
       "      <td>0.498963</td>\n",
       "      <td>0.052501</td>\n",
       "      <td>...</td>\n",
       "      <td>0.093629</td>\n",
       "      <td>0.333763</td>\n",
       "      <td>0.481617</td>\n",
       "      <td>0.250788</td>\n",
       "      <td>0.210427</td>\n",
       "      <td>0.087631</td>\n",
       "      <td>7.282721e+07</td>\n",
       "      <td>3.299300e+08</td>\n",
       "      <td>2.801232e+08</td>\n",
       "      <td>105.512891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.900000</td>\n",
       "      <td>2003.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.995062e+02</td>\n",
       "      <td>3.290000e+02</td>\n",
       "      <td>-1.995460e+08</td>\n",
       "      <td>-4014.741707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.200000</td>\n",
       "      <td>2007.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.747396e+07</td>\n",
       "      <td>3.651654e+07</td>\n",
       "      <td>8.960707e+06</td>\n",
       "      <td>0.429459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.700000</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.418978e+07</td>\n",
       "      <td>1.214685e+08</td>\n",
       "      <td>6.702649e+07</td>\n",
       "      <td>0.649071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.300000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.040258e+08</td>\n",
       "      <td>2.833599e+08</td>\n",
       "      <td>1.955832e+08</td>\n",
       "      <td>0.775034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>2022.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>242.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.032452e+08</td>\n",
       "      <td>3.678646e+09</td>\n",
       "      <td>3.380369e+09</td>\n",
       "      <td>0.999922</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Rating         Year        Month      Runtime       cert_G  \\\n",
       "count  1810.000000  1810.000000  1810.000000  1810.000000  1810.000000   \n",
       "mean      6.693702  2012.021547     7.105525   113.859116     0.011602   \n",
       "std       0.886415     5.630121     3.551473    19.378273     0.107116   \n",
       "min       1.900000  2003.000000     1.000000    75.000000     0.000000   \n",
       "25%       6.200000  2007.000000     4.000000   100.000000     0.000000   \n",
       "50%       6.700000  2012.000000     7.000000   111.000000     0.000000   \n",
       "75%       7.300000  2017.000000    10.000000   125.000000     0.000000   \n",
       "max       9.000000  2022.000000    12.000000   242.000000     1.000000   \n",
       "\n",
       "        cert_NC-17      cert_PG   cert_PG-13       cert_R   cert_TV-14  ...  \\\n",
       "count  1810.000000  1810.000000  1810.000000  1810.000000  1810.000000  ...   \n",
       "mean      0.003315     0.118785     0.385635     0.465746     0.002762  ...   \n",
       "std       0.057496     0.323624     0.486880     0.498963     0.052501  ...   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "75%       0.000000     0.000000     1.000000     1.000000     0.000000  ...   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000  ...   \n",
       "\n",
       "       contient_Africa  contient_Asia  contient_Europe  \\\n",
       "count      1810.000000    1810.000000      1810.000000   \n",
       "mean          0.008840       0.127624         0.365193   \n",
       "std           0.093629       0.333763         0.481617   \n",
       "min           0.000000       0.000000         0.000000   \n",
       "25%           0.000000       0.000000         0.000000   \n",
       "50%           0.000000       0.000000         0.000000   \n",
       "75%           0.000000       0.000000         1.000000   \n",
       "max           1.000000       1.000000         1.000000   \n",
       "\n",
       "       contient_North America  contient_Oceania  contient_South America  \\\n",
       "count             1810.000000       1810.000000             1810.000000   \n",
       "mean                 0.932597          0.046409                0.007735   \n",
       "std                  0.250788          0.210427                0.087631   \n",
       "min                  0.000000          0.000000                0.000000   \n",
       "25%                  1.000000          0.000000                0.000000   \n",
       "50%                  1.000000          0.000000                0.000000   \n",
       "75%                  1.000000          0.000000                0.000000   \n",
       "max                  1.000000          1.000000                1.000000   \n",
       "\n",
       "         Budget_inf    Income_inf    Profit_inf      ROI_inf  \n",
       "count  1.810000e+03  1.810000e+03  1.810000e+03  1810.000000  \n",
       "mean   7.149188e+07  2.378169e+08  1.663250e+08    -5.023364  \n",
       "std    7.282721e+07  3.299300e+08  2.801232e+08   105.512891  \n",
       "min    1.995062e+02  3.290000e+02 -1.995460e+08 -4014.741707  \n",
       "25%    1.747396e+07  3.651654e+07  8.960707e+06     0.429459  \n",
       "50%    4.418978e+07  1.214685e+08  6.702649e+07     0.649071  \n",
       "75%    1.040258e+08  2.833599e+08  1.955832e+08     0.775034  \n",
       "max    4.032452e+08  3.678646e+09  3.380369e+09     0.999922  \n",
       "\n",
       "[8 rows x 50 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d4bc17b0",
   "metadata": {},
   "outputs": [
    {
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amxYuXKjGjRvTIADA40yfPl333Xef4/Hnn3+uX//615o1a5b69u3rWN6kSZNae8/bbrtNOTk5iouLq7V1AoAvCAgI0I033qgNGzbo4sWLCgz872G0DRs2KCwsTB999FGZ123YsEFRUVFKSkqqy3IBwO08LRfXrl2rZ599lhP7ANxmxYoV2rVrlyZOnFjmuZycHDVr1syt718bOVdUVKRly5ZJkrKysvSf//xHTZs2dRrz3HPP6YUXXlBmZqa6deuma665ptL11sX2A/A//pK7VcExW89CYxW8UlFRkWw2m9OH95p47bXX9PXXX+vDDz90OmElXe4oLS4udml94eHh6tmzp0uvSUxMVPfu3ct9ztV1AUB1eHq21oaCggKFhobW+fsC8G21mZ9t2rRxaqj/4YcfJElt27at9Tnh+fPnFRISoiZNmtRqo1ZVGGP0ww8/qH79+nX6vgB8X23Pafv27at33nlH27Ztc+RwcXGxNm3apP/93//VH//4R+3du1cdO3aUdPnb/jk5Obr11ltls9lq9N7MXQHUBl/KxbpC/gKoCm85b/P3v/9dR48e1W233aZ3331XS5Ys0SOPPOI0ZteuXapfv75+85vfXHVdV36W95btB+A7vCV3qpK7VWHFMVtUjFsBwiV///vf1alTJ9ntdrVu3Vp/+tOflJ6e7vSh2BijhQsXqkuXLqpfv74aNWqkO++8U998843Tuvr06aPExERt3bpVN954o0JDQ9W6dWs9+eSTTifbS26rt3TpUk2ePFlNmzaV3W7X119/LUlav369brnlFoWHhys0NFQ33HCDPvjgA5e26/jx45JUYcdnvXrOu8o///lP3XXXXYqJiZHdblfz5s119913q7Cw0KnmqtwKsDLcChDwfWSrHNu7Y8cODRs2TOHh4YqIiNAvfvELHT161Ol1xcXFmjt3rjp06CC73a7o6GjdfffdOnToULm/i48//li9evVSaGiofvnLX6ply5bavXu3Nm7c6LilVunbXQHwDr6an9WRlZWl66+/XvXr11eHDh300ksvOT1fcunodevW6Ze//KWaNGmi0NBQFRYW1uiy0vn5+ZoyZYpatWql4OBgNW3aVBMnTtS5c+ecxtlsNv3mN7/R888/r44dO8put2vJkiU12WQAHsZXM7nkCwJXfr7/4osvdPLkSY0bN05xcXFOV2f59NNPdf78ecfrsrOzdfvtt6tZs2YKCQnRj370I40fP17Hjh1zep+S39Xnn3+uO++8U40aNeLK1YCXIxcvK52LkrRt2zYNGTJEkZGRCgkJUdeuXfW3v/3N6X0KCgoc88yQkBBFRkaqe/fuWrlypaTLt65+9tlnJcnpltklc1pXf7eljx2UHJP94x//qHnz5qlVq1a65pprlJKSoi1btrj0OwNQeyo7N7Nr1y7dfvvtatSokUJCQtSlS5cynz1LsnLlypWaNm2a4uPjFR4ern79+mnfvn2OcX369NG7776rgwcPOuVMifJuSZWXl6fx48erWbNmCg4OVqtWrZSRkaGLFy86xlQ1XyrLuapatGiRgoOD9fLLLyshIUEvv/yyjDFO2/HXv/5V58+fd7xHyfmoq32WL2/7//Of/2jcuHFKSEhQcHCw4uPjdeedd+rw4cOSLn95bPLkyerSpYsiIiIUGRmplJQU/f3vf3dpmwDUHXK39nNXunyea+bMmWrfvr3q16+vhg0bqlOnTvrTn/7kGFPeMduqHmOAGxigit577z1Tr14906dPH7N69Wrz2muvmeTkZNOyZUtz5Z/Svffea4KCgszkyZNNVlaWWbFihenQoYOJiYkxeXl5jnG9e/c2UVFRpm3btub555832dnZZsKECUaSWbJkiWPcRx99ZCSZpk2bmjvvvNO8/fbb5p133jHHjx83S5cuNTabzQwdOtS8+eabZs2aNWbQoEEmICDArF+/vsrb9sknnxhJ5sc//rFZvXq1OXbsWIVjd+7caa655hrTsmVL8/zzz5sPPvjALFu2zAwfPtzk5+c71fzRRx9V+t4vv/yykWS2bNliioqKnH6MMWb//v1Gknn55ZervD0AvAfZetmMGTOMJNOiRQvz4IMPmvfff9/MmzfPhIWFma5du5oLFy44xo4bN85IMr/5zW9MVlaWef75502TJk1MQkKCOXr0qNPvIjIy0iQkJJjMzEzz0UcfmY0bN5rPP//ctG7d2nTt2tXk5OSYnJwc8/nnn1d5uwB4Bl/OzyuVvN9rr71W7vMtWrQwzZo1M9dee6155ZVXzPvvv29+9rOfGUlm48aNjnElc86mTZuacePGmffee8+8/vrr5uLFi47n9u/f7xg/evRo06JFizLvNXr0aMfjc+fOmS5dupjGjRubefPmmfXr15s//elPJiIiwtx8882muLjYMbbkvTt16mRWrFhhPvzwQ7Nr165q/U4AeB5fzuRLly6ZRo0amdTUVMeyp59+2sTFxRljjBkxYoT52c9+5nguIyPDSDK7d+82xhjz3HPPmdmzZ5u3337bbNy40SxZssR07tzZtG/f3mmOe+V8+Pe//73Jzs42b731lgv/FQB4EnKx4lz88MMPTXBwsLnxxhvNq6++arKyssyYMWPKHP8cP368CQ0NNfPmzTMfffSReeedd8yTTz5pMjMzjTHGfP311+bOO+80khyf7XNycswPP/zg8u+2vGMHJcdkW7ZsaQYOHGjeeust89Zbb5mkpCTTqFEjc+rUqSr/zgDUjsrOzfzzn/80DRo0MG3atDGvvPKKeffdd81dd91lJJk5c+Y41lOSlS1btjQ///nPzbvvvmtWrlxpmjdvbtq2bWsuXrxojDFm9+7d5oYbbjCxsbFOOVNCkpkxY4bjcW5urklISDAtWrQwL7zwglm/fr15/PHHjd1uN2PGjHGMq2q+VJZzVfHdd9+ZevXqOXL5D3/4g5FkNmzY4BiTk5Njbr31VlO/fn3Hexw5csSxjRV9li+9/YcOHTJxcXFOxwheffVV88tf/tLs3bvXGGPMqVOnzJgxY8zSpUvNhx9+aLKyssyUKVNMvXr1nP7/DIBnIHfdk7vGGDN79mwTEBBgZsyYYT744AOTlZVlnnnmGZOenu4YU94x26oeY0Dto7EKVfbjH//YJCQkmMLCQseyM2fOmKioKMcBgZycHCPJPP30006v/e6770z9+vXNQw895FjWu3dvI8l8+umnTmOvvfZaM2DAAMfjkrC96aabnMadO3fOREZGmsGDBzstv3TpkuncubPp0aOHS9v32GOPmeDgYCPJSDKtWrUy9913n/niiy+cxt18882mYcOGjollearTWFXeT1FREY1VgI8jWy8rOZH0u9/9zmn58uXLjSSzbNkyY4wxe/fuNZLMhAkTnMZ9+umnRpJ55JFHyvwuPvjggzJ1XXfddaZ3794ubQsAz+Lr+Vn6/a7WWBUSEmIOHjzoWHb+/HkTGRlpxo8f71hWMue8++67y6yjuo1Vs2fPNvXq1TNbt251Gvf6668bSWbt2rWOZZJMRESEOXHiRFU2G4CX8fVMHjp0qAkLC3N8AWrw4MHmf/7nf4wxxixcuNA0adLE0Uzat29fEx0dXe56iouLTVFRkTl48KCRZP7+9787niuZDz/66KMu1QbAM5GLFedihw4dTNeuXR2vLTFo0CATFxdnLl26ZIwxJjEx0QwdOvSqdfz61792alQrUZ3fbeljByXHZJOSkhwn+4wx5rPPPjOSzMqVK69aG4DaV9m5mf/5n/8xdrvdfPvtt07L09LSTGhoqOPEeUlW3nrrrU7j/va3vzlOppe47bbbynw2LlH6BP/48ePNNddc4/T53Bhj/vjHPzo1mLqSLxXlXFU99thjRpLJysoyxhjzzTffGJvNZkaNGuU0bvTo0SYsLKzcbazos3zp7f/lL39pgoKCzJ49e6pc38WLF01RUZEZO3as6dq1a5VfB6BukLuuq2ruDho0yHTp0uWq6yrvmO2VrnaMAbWPWwGiSs6dO6dt27Zp6NChCg4Odiy/5pprNHjwYMfjd955RzabTb/4xS908eJFx09sbKw6d+5c5tZ4sbGx6tGjh9OyTp066eDBg2VquOOOO5web968WSdOnNDo0aOd3qu4uFgDBw7U1q1by9yC5GqmT5+ub7/9Vi+99JLGjx+va665Rs8//7y6devmuMR0QUGBNm7cqOHDh7t0T1NjjFONV15+sMQrr7yirVu3Ov0EBgZW+T0AeB+ydWWZ8T//+c+dHg8fPlyBgYGO2wiU/O+YMWOcxvXo0UMdO3YscwuDRo0a6eabb65yvQC8gz/kpyu6dOmi5s2bOx6HhISoXbt2Vaq7Jt555x0lJiaqS5cuTts8YMCAcm+LffPNN6tRo0a19v4APIM/ZHLfvn117tw5bd26VcXFxdq0aZP69OkjSerdu7eOHj2q3bt3q7CwUFu2bHG63dWRI0d03333KSEhQYGBgQoKClKLFi0kSXv37q10WwB4H3Kx4lz8+uuv9c9//tPx2f/KWm699Vbl5uY6bgfTo0cPvffee3r44Ye1YcMGnT9/vsr1ufq7vdqxg9tuu00BAQGOx506dZKkcn/vANynKudmPvzwQ91yyy1KSEhwWj5mzBgVFBQoJyfHafmQIUOcHtd0/37nnXfUt29fxcfHO2VPWlqaJGnjxo1O492dL8YYx22o+vfvL0lq1aqV+vTpozfeeEP5+flVWk9VP8u/99576tu3rzp27HjVca+99ppuuOEGXXPNNY758aJFi8qdGwOwDrnrOldyt0ePHvriiy80YcIEvf/++1XOZFePMaD20LWBKjl58qSMMYqJiSnz3JXLDh8+XOE4SWrdurXT46ioqDJj7HZ7uR+U4+LinB6X3JP5zjvvrLDuEydOKCwsrMLnS4uJidE999yje+65R5L08ccfKy0tTQ888IDuuusunTx5UpcuXVKzZs2qvE7pcnBfeWBVkvbv36+WLVs6Hnfs2FHdu3d3ab0AvBv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      "text/plain": [
       "<Figure size 3000x2000 with 56 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "df.hist(bins=40, figsize=(30,20)) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0dc2b409",
   "metadata": {},
   "outputs": [],
   "source": [
    "count_bud = (df['Budget_inf'] > 10**6).sum()\n",
    "count_inc = (df['Income_inf'] > 10**6).sum()\n",
    "count_pro = (df['Profit_inf'] > 10**5).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d07e5277",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Amount of Budgets having more than 1M: 1737\n",
      "Amount of Incomes having more than 1M 1741\n",
      "Amount of Profit made more than 10K: 1552\n"
     ]
    }
   ],
   "source": [
    "print(f'Amount of Budgets having more than 1M: {count_bud}')\n",
    "print(f'Amount of Incomes having more than 1M {count_inc}')\n",
    "print(f'Amount of Profit made more than 10K: {count_pro}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5240c3c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1810, 50)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "134bb632",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of films rated more than 8: 78\n"
     ]
    }
   ],
   "source": [
    "count_rating = (df['Rating'] > 8).sum()\n",
    "print(f'Number of films rated more than 8: {count_rating}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a4b7c873",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f86f3030040>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IWQAAAAYQsgAAAAwgZAEAABhAyAIAADCAkAUAAGAAIQsAAMAAQhYAAIABhCwAAAADCFkAAAAG3FDIKisrk8PhUFFRkb/Nsix5PB6lpqZqzJgxysvL04kTJwLW83q9WrFihVJSUjR27FjNnz9fFy5cuJGhAAAADCkhh6yDBw9qy5YtuvvuuwPa161bp/Xr12vjxo06ePCg3G63CgoK1Nra6u9TVFSkqqoqVVZWav/+/bp8+bLmzp2rzs7O0I8EAABgCAkpZF2+fFmPPvqoXn75ZSUmJvrbLctSeXm51qxZo4ULFyojI0M7duxQW1ubdu3aJUlqbm7W1q1b9dJLL2nWrFmaNm2adu7cqWPHjmn37t3hOSoAAIBBFhXKSk8++aT+/M//XLNmzdJzzz3nbz979qwaGhpUWFjob4uJidEDDzygAwcOaNmyZaqrq5PP5wvok5qaqoyMDB04cECzZ8+27c/r9crr9fqXW1paJEk+n08+ny+UQxgcXfYrdRGRVxURedX/d1Ai+j724VSer+s+r8Pq/BpGTeyoiR01saMmdtTErq+ahFKroENWZWWlDh8+rIMHD9rea2hokCS5XK6AdpfLpXPnzvn7REdHB1wB6+7Tvf61ysrKtHbtWlt7dXW14uLigj2EISWr4P/+znzoUNi3/9ZbYd/kgKqpqRnsIQw51MSOmthREztqYkdN7HqrSVtbW9DbCipkffzxx/rRj36k6upqxcbG9trP4XAELFuWZWu71vX6lJSUqLi42L/c0tKitLQ0FRYWKiEhIYgjGGQnT9uayl5OVETkVWU+dEjH9kxXV2cQp2RCUp9dSkqCGeDQ4fP5VFNTo4KCAjmdzsEezpBATeyoiR01saMmdtTErq+adN9FC0ZQIauurk6NjY3Kzs72t3V2dmrfvn3auHGjTp06Jemrq1UTJ07092lsbPRf3XK73ero6FBTU1PA1azGxkbl5ub2uN+YmBjFxMTY2p1O5/CaHBGRtqavh6quzqjgQlZX38c+nMrTk2F3jgcANbGjJnbUxI6a2FETu95qEkqdgnrwPT8/X8eOHdPRo0f9r+nTp+vRRx/V0aNHdfvtt8vtdgdcauvo6FBtba0/QGVnZ8vpdAb0qa+v1/Hjx3sNWQAAAMNNUFey4uPjlZGREdA2duxYJScn+9uLiopUWlqq9PR0paenq7S0VHFxcVq8eLEkady4cVq6dKlWrlyp5ORkJSUladWqVcrMzNSsWbPCdFgAAACDK6RPF17P6tWr1d7eruXLl6upqUkzZsxQdXW14uPj/X02bNigqKgoLVq0SO3t7crPz9f27dsVGWm/nQYAADAc3XDI2rt3b8Cyw+GQx+ORx+PpdZ3Y2FhVVFSooqLiRncPAAAwJPHbhQAAAAYQsgAAAAwgZAEAABhAyAIAADCAkAUAAGAAIQsAAMAAQhYAAIABhCwAAAADCFkAAAAGELIAAAAMIGQBAAAYQMgCAAAwgJAFAABgACELAADAAEIWAACAAYQsAAAAAwhZAAAABhCyAAAADCBkAQAAGEDIAgAAMICQBQAAYAAhCwAAwABCFgAAgAGELAAAAAMIWQAAAAYQsgAAAAwgZAEAABhAyAIAADCAkAUAAGAAIQsAAMAAQhYAAIABhCwAAAADCFkAAAAGELIAAAAMIGQBAAAYQMgCAAAwgJAFAABgACELAADAAEIWAACAAYQsAAAAAwhZAAAABhCyAAAADCBkAQAAGEDIAgAAMICQBQAAYAAhCwAAwABCFgAAgAGELAAAAAMIWQAAAAYQsgAAAAwgZAEAABhAyAIAADAgqJC1efNm3X333UpISFBCQoJycnL09ttv+9+3LEsej0epqakaM2aM8vLydOLEiYBteL1erVixQikpKRo7dqzmz5+vCxcuhOdoAAAAhoigQtYtt9yin/70pzp06JAOHTqkhx56SI888og/SK1bt07r16/Xxo0bdfDgQbndbhUUFKi1tdW/jaKiIlVVVamyslL79+/X5cuXNXfuXHV2dob3yAAAAAZRUCFr3rx5evjhhzV16lRNnTpVzz//vG666Sa99957sixL5eXlWrNmjRYuXKiMjAzt2LFDbW1t2rVrlySpublZW7du1UsvvaRZs2Zp2rRp2rlzp44dO6bdu3cbOUAAAIDBEBXqip2dnXrttdd05coV5eTk6OzZs2poaFBhYaG/T0xMjB544AEdOHBAy5YtU11dnXw+X0Cf1NRUZWRk6MCBA5o9e3aP+/J6vfJ6vf7llpYWSZLP55PP5wv1EAZel/1qXUTkVUVEXvX/HZSIvo99OJXn67rP67A6v4ZREztqYkdN7KiJHTWx66smodQq6JB17Ngx5eTk6Msvv9RNN92kqqoq3XnnnTpw4IAkyeVyBfR3uVw6d+6cJKmhoUHR0dFKTEy09WloaOh1n2VlZVq7dq2tvbq6WnFxccEewpCSVfB/f2c+dCjs23/rrbBvckDV1NQM9hCGHGpiR03sqIkdNbGjJna91aStrS3obQUdsu644w4dPXpUX3zxhV5//XUtWbJEtbW1/vcdDkdAf8uybG3X6qtPSUmJiouL/cstLS1KS0tTYWGhEhISgj2EwXPytK2p7OVERUReVeZDh3Rsz3R1dQZxSiYk9dmlpCSYAQ4dPp9PNTU1KigokNPpHOzhDAnUxI6a2FETO2piR03s+qpJ9120YAQdsqKjo/XNb35TkjR9+nQdPHhQP/vZz/T3f//3kr66WjVx4kR//8bGRv/VLbfbrY6ODjU1NQVczWpsbFRubm6v+4yJiVFMTIyt3el0Dq/JERFpa/p6qOrqjAouZHX1fezDqTw9GXbneABQEztqYkdN7KiJHTWx660modTphr8ny7Iseb1eTZ48WW63O+AyW0dHh2pra/0BKjs7W06nM6BPfX29jh8/ft2QBQAAMNwEdSXrmWee0Zw5c5SWlqbW1lZVVlZq7969euedd+RwOFRUVKTS0lKlp6crPT1dpaWliouL0+LFiyVJ48aN09KlS7Vy5UolJycrKSlJq1atUmZmpmbNmmXkAAEAAAZDUCHrs88+02OPPab6+nqNGzdOd999t9555x0VFHz19Pbq1avV3t6u5cuXq6mpSTNmzFB1dbXi4+P929iwYYOioqK0aNEitbe3Kz8/X9u3b1dkpP1WGgAAwHAVVMjaunXrdd93OBzyeDzyeDy99omNjVVFRYUqKiqC2TUAAMCwwm8XAgAAGEDIAgAAMICQBQAAYAAhCwAAwABCFgAAgAGELAAAAAMIWQAAAAYQsgAAAAwgZAEAABhAyAIAADCAkAUAAGAAIQsAAMAAQhYAAIABhCwAAAADCFkAAAAGELIAAAAMIGQBAAAYQMgCAAAwgJAFAABgACELAADAAEIWAACAAYQsAAAAAwhZAAAABhCyAAAADCBkAQAAGEDIAgAAMICQBQAAYAAhCwAAwABCFgAAgAGELAAAAAMIWQAAAAYQsgAAAAwgZAEAABhAyAIAADCAkAUAAGAAIQsAAMAAQhYAAIABhCwAAAADCFkAAAAGELIAAAAMIGQBAAAYQMgCAAAwgJAFAABgACELAADAAEIWAACAAYQsAAAAAwhZAAAABhCyAAAADCBkAQAAGEDIAgAAMICQBQAAYAAhCwAAwICgQlZZWZnuuecexcfHa8KECVqwYIFOnToV0MeyLHk8HqWmpmrMmDHKy8vTiRMnAvp4vV6tWLFCKSkpGjt2rObPn68LFy7c+NEAAAAMEUGFrNraWj355JN67733VFNTo6tXr6qwsFBXrlzx91m3bp3Wr1+vjRs36uDBg3K73SooKFBra6u/T1FRkaqqqlRZWan9+/fr8uXLmjt3rjo7O8N3ZAAAAIMoKpjO77zzTsDytm3bNGHCBNXV1em73/2uLMtSeXm51qxZo4ULF0qSduzYIZfLpV27dmnZsmVqbm7W1q1b9corr2jWrFmSpJ07dyotLU27d+/W7Nmzw3RoAAAAgyeokHWt5uZmSVJSUpIk6ezZs2poaFBhYaG/T0xMjB544AEdOHBAy5YtU11dnXw+X0Cf1NRUZWRk6MCBAz2GLK/XK6/X619uaWmRJPl8Pvl8vhs5hIHVZb9SFxF5VRGRV/1/ByWi72MfTuX5uu7zOqzOr2HUxI6a2FETO2piR03s+qpJKLUKOWRZlqXi4mLdf//9ysjIkCQ1NDRIklwuV0Bfl8ulc+fO+ftER0crMTHR1qd7/WuVlZVp7dq1tvbq6mrFxcWFeghDQlbB//2d+dChsG//rbfCvskBVVNTM9hDGHKoiR01saMmdtTEjprY9VaTtra2oLcVcsh66qmn9P7772v//v229xwOR8CyZVm2tmtdr09JSYmKi4v9yy0tLUpLS1NhYaESEhJCGP0gOXna1lT2cqIiIq8q86FDOrZnuro6gzglE5LCOLjQlZSEf5s+n081NTUqKCiQ0+kM/w6GIWpiR03sqIkdNbGjJnZ91aT7LlowQgpZK1as0K9+9Svt27dPt9xyi7/d7XZL+upq1cSJE/3tjY2N/qtbbrdbHR0dampqCria1djYqNzc3B73FxMTo5iYGFu70+kcXpMjItLW9PVQ1dUZFVzI6hoax27yFAy7czwAqIkdNbGjJnbUxI6a2PVWk1DqFNSnCy3L0lNPPaU33nhDe/bs0eTJkwPenzx5stxud8Clto6ODtXW1voDVHZ2tpxOZ0Cf+vp6HT9+vNeQBQAAMNwEdSXrySef1K5du/Qf//Efio+P9z9DNW7cOI0ZM0YOh0NFRUUqLS1Venq60tPTVVpaqri4OC1evNjfd+nSpVq5cqWSk5OVlJSkVatWKTMz0/9pQwAAgOEuqJC1efNmSVJeXl5A+7Zt2/TXf/3XkqTVq1ervb1dy5cvV1NTk2bMmKHq6mrFx8f7+2/YsEFRUVFatGiR2tvblZ+fr+3btysy0n47DQAAYDgKKmRZltVnH4fDIY/HI4/H02uf2NhYVVRUqKKiIpjdAwAADBv8diEAAIABhCwAAAADCFkAAAAGELIAAAAMIGQBAAAYQMgCAAAwgJAFAABgACELAADAAEIWAACAAYQsAAAAAwhZAAAABhCyAAAADCBkAQAAGEDIAgAAMICQBQAAYEDUYA8AN+CzS+Hblis5fNsCAABcyQIAADCBkAUAAGAAIQsAAMAAQhYAAIABhCwAAAADCFkAAAAGELIAAAAMIGQBAAAYQMgCAAAwgJAFAABgACELAADAAEIWAACAAYQsAAAAAwhZAAAABhCyAAAADCBkAQAAGEDIAgAAMICQBQAAYAAhCwAAwABCFgAAgAGELAAAAAMIWQAAAAYQsgAAAAwgZAEAABhAyAIAADCAkAUAAGAAIQsAAMAAQhYAAIABhCwAAAADCFkAAAAGELIAAAAMIGQBAAAYQMgCAAAwgJAFAABgACELAADAAEIWAACAAUGHrH379mnevHlKTU2Vw+HQm2++GfC+ZVnyeDxKTU3VmDFjlJeXpxMnTgT08Xq9WrFihVJSUjR27FjNnz9fFy5cuKEDAQAAGEqCDllXrlxRVlaWNm7c2OP769at0/r167Vx40YdPHhQbrdbBQUFam1t9fcpKipSVVWVKisrtX//fl2+fFlz585VZ2dn6EcCAAAwhEQFu8KcOXM0Z86cHt+zLEvl5eVas2aNFi5cKEnasWOHXC6Xdu3apWXLlqm5uVlbt27VK6+8olmzZkmSdu7cqbS0NO3evVuzZ8+2bdfr9crr9fqXW1paJEk+n08+ny/YQxg8XfYQGRF5VRGRV/1/D5qI0Oto4hR0n9dhdX4NoyZ21MSOmthREztqYtdXTUKplcOyLCvUATkcDlVVVWnBggWSpDNnzmjKlCk6fPiwpk2b5u/3yCOPaPz48dqxY4f27Nmj/Px8ff7550pMTPT3ycrK0oIFC7R27VrbfjweT4/tu3btUlxcXKjDBwAA6Je2tjYtXrxYzc3NSkhI6Nc6QV/Jup6GhgZJksvlCmh3uVw6d+6cv090dHRAwOru073+tUpKSlRcXOxfbmlpUVpamgoLC/t9oEPCydO2prKXExUReVWZDx3SsT3T1dUZ1lPSfxOSQl61pCSM4/gjn8+nmpoaFRQUyOl0hn8HwxA1saMmdtTEjprYURO7vmrSfRctGEb+i+5wOAKWLcuytV3ren1iYmIUExNja3c6ncNrckRE2pq+Hqq6OqMGL2R1hV5Hk6dg2J3jAUBN7KiJHTWxoyZ21MSut5qEUqewfoWD2+2WJNsVqcbGRv/VLbfbrY6ODjU1NfXaBwAAYLgLa8iaPHmy3G63ampq/G0dHR2qra1Vbm6uJCk7O1tOpzOgT319vY4fP+7vAwAAMNwFfW/q8uXL+uijj/zLZ8+e1dGjR5WUlKRbb71VRUVFKi0tVXp6utLT01VaWqq4uDgtXrxYkjRu3DgtXbpUK1euVHJyspKSkrRq1SplZmb6P20IAAAw3AUdsg4dOqQHH3zQv9z9QPqSJUu0fft2rV69Wu3t7Vq+fLmampo0Y8YMVVdXKz4+3r/Ohg0bFBUVpUWLFqm9vV35+fnavn27IiPtzywBAAAMR0GHrLy8PF3vWx8cDoc8Ho88Hk+vfWJjY1VRUaGKiopgdw8AADAs8NuFAAAABhCyAAAADCBkAQAAGEDIAgAAMICQBQAAYAAhCwAAwABCFgAAgAGELAAAAAMIWQAAAAYQsgAAAAwgZAEAABgQ9G8XAte6zs9UhiwiQsrKksrKpK4uM/sAAMAkrmQBAAAYQMgCAAAwgJAFAABgACELAADAAEIWAACAAYQsAAAAAwhZAAAABhCyAAAADCBkAQAAGEDIAgAAMICQBQAAYAAhCwAAwABCFgAAgAGELAAAAAOiBnsAQH94PMN7+wCA0YeQha98dim823Mlh3d7AAAMM9wuBAAAMICQBQAAYAAhCwAAwACeyQI0MA++83A9AIwuXMkCAAAwgJAFAABgACELAADAAEIWAACAAYQsAAAAAwhZAAAABvAVDtcR9o/cf5YU5g0CAIChiitZAAAABhCyAAAADCBkAQAAGMAzWTDjs0s3tn7k1a/+2fi5lOK68fEAADDACFnAALnRD1JEREhZWVJZmdTVZWYfAIDw4XYhAACAAYQsAAAAA7hd2JcbfbYIGECmbxdyOxIA+o+QhaEv3EHXlRze7QEA0ANuFwIAABhAyAIAADCA24UA+m0gnsniuS8AI8WgXsnatGmTJk+erNjYWGVnZ+u3v/3tYA4HAAAgbAbtStarr76qoqIibdq0Sffdd5/+5V/+RXPmzNHJkyd16623DtawgODwUD4AoBeDFrLWr1+vpUuX6vvf/74kqby8XP/1X/+lzZs3q6ysbLCGhdFgKH8tx/XG9vWfGursx7+6wzSwBXO7sD/fgn+j+wgFt1UBSIMUsjo6OlRXV6enn346oL2wsFAHDhyw9fd6vfJ6vf7l5uZmSdLnn38un89ncJySOpuMbb9bhHVVbW1t6rjapK4uHpOTqElPgq7Jp+bnbshSEsOymYgI31c1+fR0UPPkmf93nTfDNDbTnnmm5/aICJ/uuqtNa9deUleX84b2sXLlDa0+ZPhOfqS2tjZdeu+wnBGRN7axb00Oz6AGmc/31b87ly5dktN5Y/Okv156yez2b3S+9lWT1tZWSZJlWf3fqDUIPvnkE0uS9bvf/S6g/fnnn7emTp1q6/+Tn/zEksSLFy9evHjx4jWor48//rjfeWdQLxE4HI6AZcuybG2SVFJSouLiYv9yV1eXPv/8cyUnJ/fYf7hpaWlRWlqaPv74YyUkJAz2cIYEamJHTeyoiR01saMmdtTErq+aWJal1tZWpaam9nubgxKyUlJSFBkZqYaGhoD2xsZGuVwuW/+YmBjFxMQEtI0fP97kEAdFQkICk/0a1MSOmthREztqYkdN7KiJ3fVqMm7cuKC2NShf4RAdHa3s7GzV1NQEtNfU1Cg3N3cwhgQAABBWg3a7sLi4WI899pimT5+unJwcbdmyRefPn9cTTzwxWEMCAAAIm0ELWd/73vd06dIlPfvss6qvr1dGRobeeustTZo0abCGNGhiYmL0k5/8xHZLdDSjJnbUxI6a2FETO2piR03sTNTEYVnBfBYRAAAA/cEPRAMAABhAyAIAADCAkAUAAGAAIQsAAMAAQhYAAIABhKwBsmnTJk2ePFmxsbHKzs7Wb3/72+v2r62tVXZ2tmJjY3X77bfrn//5nwdopAMnmJrs3btXDofD9vqf//mfARyxWfv27dO8efOUmpoqh8OhN998s891Rvo8CbYmI32elJWV6Z577lF8fLwmTJigBQsW6NSpU32uN5LnSSg1GenzZPPmzbr77rv931yek5Ojt99++7rrjOQ5IgVfk3DNEULWAHj11VdVVFSkNWvW6MiRI5o5c6bmzJmj8+fP99j/7NmzevjhhzVz5kwdOXJEzzzzjH74wx/q9ddfH+CRmxNsTbqdOnVK9fX1/ld6evoAjdi8K1euKCsrSxs3buxX/9EwT4KtSbeROk9qa2v15JNP6r333lNNTY2uXr2qwsJCXblypdd1Rvo8CaUm3UbqPLnlllv005/+VIcOHdKhQ4f00EMP6ZFHHtGJEyd67D/S54gUfE263fAc6fdPSSNkf/qnf2o98cQTAW3f+ta3rKeffrrH/qtXr7a+9a1vBbQtW7bMuvfee42NcaAFW5N3333XkmQ1NTUNwOgGnySrqqrqun1Gwzz5uv7UZLTNk8bGRkuSVVtb22uf0TZP+lOT0TZPLMuyEhMTrX/913/t8b3RNke6Xa8m4ZojXMkyrKOjQ3V1dSosLAxoLyws1IEDB3pc57//+79t/WfPnq1Dhw7J5/MZG+tACaUm3aZNm6aJEycqPz9f7777rslhDnkjfZ7ciNEyT5qbmyVJSUlJvfYZbfOkPzXpNhrmSWdnpyorK3XlyhXl5OT02Ge0zZH+1KTbjc4RQpZhFy9eVGdnp1wuV0C7y+VSQ0NDj+s0NDT02P/q1au6ePGisbEOlFBqMnHiRG3ZskWvv/663njjDd1xxx3Kz8/Xvn37BmLIQ9JInyehGE3zxLIsFRcX6/7771dGRkav/UbTPOlvTUbDPDl27JhuuukmxcTE6IknnlBVVZXuvPPOHvuOljkSTE3CNUcG7bcLRxuHwxGwbFmWra2v/j21D2fB1OSOO+7QHXfc4V/OycnRxx9/rH/8x3/Ud7/7XaPjHMpGwzwJxmiaJ0899ZTef/997d+/v8++o2We9Lcmo2Ge3HHHHTp69Ki++OILvf7661qyZIlqa2t7DRWjYY4EU5NwzRGuZBmWkpKiyMhI2xWaxsZG2/9z6OZ2u3vsHxUVpeTkZGNjHSih1KQn9957rz788MNwD2/YGOnzJFxG4jxZsWKFfvWrX+ndd9/VLbfcct2+o2WeBFOTnoy0eRIdHa1vfvObmj59usrKypSVlaWf/exnPfYdLXMkmJr0JJQ5QsgyLDo6WtnZ2aqpqQlor6mpUW5ubo/r5OTk2PpXV1dr+vTpcjqdxsY6UEKpSU+OHDmiiRMnhnt4w8ZInyfhMpLmiWVZeuqpp/TGG29oz549mjx5cp/rjPR5EkpNejKS5klPLMuS1+vt8b2RPkd6c72a9CSkOXJDj82jXyorKy2n02lt3brVOnnypFVUVGSNHTvW+t///V/Lsizr6aefth577DF//zNnzlhxcXHWj3/8Y+vkyZPW1q1bLafTaf3yl78crEMIu2BrsmHDBquqqsr64IMPrOPHj1tPP/20Jcl6/fXXB+sQwq61tdU6cuSIdeTIEUuStX79euvIkSPWuXPnLMsanfMk2JqM9Hnyd3/3d9a4ceOsvXv3WvX19f5XW1ubv89omyeh1GSkz5OSkhJr37591tmzZ63333/feuaZZ6yIiAirurrasqzRN0csK/iahGuOELIGyD/90z9ZkyZNsqKjo60/+ZM/Cfh48ZIlS6wHHnggoP/evXutadOmWdHR0dZtt91mbd68eYBHbF4wNXnhhResKVOmWLGxsVZiYqJ1//33W//5n/85CKM2p/sjw9e+lixZYlnW6JwnwdZkpM+Tnmohydq2bZu/z2ibJ6HUZKTPk7/927/1/2/rzTffbOXn5/vDhGWNvjliWcHXJFxzxGFZf3y6DQAAAGHDM1kAAAAGELIAAAAMIGQBAAAYQMgCAAAwgJAFAABgACELAADAAEIWAACAAYQsAAAw7O3bt0/z5s1TamqqHA6H3nzzzaC38e///u/6zne+o7i4OE2aNEkvvvjiDY2JkAUAAIa9K1euKCsrSxs3bgxp/bfffluPPvqonnjiCR0/flybNm3S+vXrQ96eJPGN7wAAYERxOByqqqrSggUL/G0dHR36h3/4B/3bv/2bvvjiC2VkZOiFF15QXl6eJGnx4sXy+Xx67bXX/OuUl5frpZde0vnz5+VwOIIeB1eyAADAiPc3f/M3+t3vfqfKykq9//77+ou/+Av92Z/9mT788ENJktfrVWxsbMA6Y8aM0YULF3Tu3LmQ9knIAgAAI9rp06f1i1/8Qq+99ppmzpypKVOmaNWqVbr//vu1bds2SdLs2bP1xhtv6De/+Y26urr0wQcfqLy8XJJUX18f0n6jwnUAAAAAQ9Hhw4dlWZamTp0a0O71epWcnCxJ+sEPfqDTp09r7ty58vl8SkhI0I9+9CN5PB5FRkaGtF9CFgAAGNG6uroUGRmpuro6W2C66aabJH31HNcLL7yg0tJSNTQ06Oabb9ZvfvMbSdJtt90W0n4JWQAAYESbNm2aOjs71djYqJkzZ163b2RkpL7xjW9Ikn7xi18oJydHEyZMCGm/hCwAADDsXb58WR999JF/+ezZszp69KiSkpI0depUPfroo3r88cf10ksvadq0abp48aL27NmjzMxMPfzww7p48aJ++ctfKi8vT19++aW2bdum1157TbW1tSGPia9wAAAAw97evXv14IMP2tqXLFmi7du3y+fz6bnnntPPf/5zffLJJ0pOTlZOTo7Wrl2rzMxMXbx4UfPmzdOxY8dkWZZycnL0/PPPa8aMGSGPiZAFAABgAF/hAAAAYAAhCwAAwABCFgAAgAGELAAAAAMIWQAAAAYQsgAAAAwgZAEAABhAyAIAADCAkAUAAGAAIQsAAMAAQhYAAIAB/x9IU5iivQU/RgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Observing one of the instances, Drama category. Contains almost same amount of data btw. two classes.\n",
    "\n",
    "plt.figure(figsize=(7,5))\n",
    "df[df['genre_Drama']==1]['Profit_inf'].hist(bins=20,  color='pink', alpha=0.7)\n",
    "df[df['genre_Drama']==0]['Profit_inf'].hist(bins=20,  color='blue', alpha=0.5)\n",
    "\n",
    "plt.legend([1,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7f0fdb9d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Budget_inf', ylabel='Income_inf'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind=\"scatter\", x='Budget_inf', y='Income_inf', alpha=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "55f5eec9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='ROI_inf', ylabel='Budget_inf'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='scatter', x='ROI_inf', y='Budget_inf', alpha=0.4, c= 'Rating', cmap=plt.get_cmap(\"jet\"), colorbar=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b773794",
   "metadata": {},
   "source": [
    "## 1. Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ebca069f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# assuming your dataframe is named 'df' and our column we want to predict is 'Rating' column\n",
    "X = df.drop('Rating', axis=1)\n",
    "y = df['Rating']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0b4ed336",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Errorif y_type in [\"binary\", \"multiclass\"]:  Because I tried to predict y = df[['Rating', 'Profit_inf']]at the same time. \n",
    "#ValueError: continuous-multioutput is not supported"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "43e8f5af",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the df to train and test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e8744a3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RobustScaler()"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# scaling w RobustScaler object and fit to training data\n",
    "scaler = RobustScaler()\n",
    "scaler.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d61789a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# apply the scaler to both the training and testing data\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "99975304",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "lr.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "98114f05",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred =lr.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a11471e0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# evaluate the model performance using mean absolute error and mean squared error and RMSE\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "RMSE = np.sqrt(mse)\n",
    "R2 = r2_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b6cb1495",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean abslute error: 0.5694339610753175\n",
      "Mean squared error: 0.562488468379647\n",
      "RMSE: 0.7499923122136967\n",
      "R2 score: 0.3289177133508395\n"
     ]
    }
   ],
   "source": [
    "print(f'Mean abslute error: {mae}')\n",
    "print(f'Mean squared error: {mse}')\n",
    "print(f'RMSE: {RMSE}')\n",
    "print(f'R2 score: {R2}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c467fe8",
   "metadata": {},
   "source": [
    "## 2. SVR (Support Vector Machine Regressor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8ab101f",
   "metadata": {},
   "outputs": [],
   "source": [
    "##SVR performs better on regression problems whereas SVM on classification problems. Therfore we continue with SVR "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "59bd7add",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import LinearSVR "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "546a988a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Scaling the data for SVM model\n",
    "scaler = RobustScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1abbf527",
   "metadata": {},
   "outputs": [],
   "source": [
    "#fitting the training data for SVM model\n",
    "svm_reg= LinearSVR(epsilon=1.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ddb1451d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ecombo/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LinearSVR(epsilon=1.5)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_reg.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5c0c2f26",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = svm_reg.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b0a03575",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Performance metrics for SVR\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "RMSE = np.sqrt(mse)\n",
    "R2 = r2_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "261f4335",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error: 0.6774149235697636\n",
      "Mean squared error: 0.830017892596901\n",
      "RMSE: 0.9110531776997987\n",
      "R2 score: 0.00973915620311716\n"
     ]
    }
   ],
   "source": [
    "print(f'Mean absolute error: {mae}')\n",
    "print(f'Mean squared error: {mse}')\n",
    "print(f'RMSE: {RMSE}')\n",
    "print(f'R2 score: {R2}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03c69b3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# confusion matrix is not gonna work continous column. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a575d67",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d1737c64",
   "metadata": {},
   "source": [
    "## 3. Decision Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0deac0e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Voting and Bagging regressors on Ensemble methods help the models to reduce overfitting therefore we apply with Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b400a115",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a decision tree model\n",
    "dtree = DecisionTreeRegressor(max_depth=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b1a791b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(max_depth=3)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtree.fit(X_train, y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ed1496b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a bagging regressor with decision tree models\n",
    "bagging_model = BaggingRegressor(base_estimator=tree_model, n_estimators=10, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1ed98e9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Approach is to use the same training algorithm for every predictor and train them on different random subsets of the training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "04f5950b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a voting regressor with decision tree models\n",
    "voting_model = VotingRegressor([('tree1', tree_model), ('tree2', tree_model), ('tree3', tree_model)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "5df180e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VotingRegressor(estimators=[('tree1', DecisionTreeRegressor(max_depth=3)),\n",
       "                            ('tree2', DecisionTreeRegressor(max_depth=3)),\n",
       "                            ('tree3', DecisionTreeRegressor(max_depth=3))])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the models on the training data\n",
    "dtree.fit(X_train, y_train)\n",
    "bagging_model.fit(X_train, y_train)\n",
    "voting_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "09b3e1d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions on the testing data\n",
    "y_pred_dtree = dtree.predict(X_test)\n",
    "y_pred_bagging = bagging_model.predict(X_test)\n",
    "y_pred_voting = voting_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f060f8f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE DecisionTree: 0.644640304962357\n",
      "MSE Bagging :0.6033145815592388\n",
      "MSE Voting :0.6446403049623571\n"
     ]
    }
   ],
   "source": [
    "# Calculate the mean squared error of the predictions\n",
    "mse_dtree = mean_squared_error(y_test, y_pred_dtree)\n",
    "mse_bagging = mean_squared_error(y_test, y_pred_bagging)\n",
    "mse_voting = mean_squared_error(y_test, y_pred_voting)\n",
    "\n",
    "print(f'MSE DecisionTree: {mse_dtree}')\n",
    "print(f'MSE Bagging :{mse_bagging}')\n",
    "print(f'MSE Voting :{mse_voting}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "e8bf81b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE Decision tree_ 0.8028949526322587\n",
      "RMSE Bagging :0.7767332756868595\n",
      "RMSE Voting :0.8028949526322589\n"
     ]
    }
   ],
   "source": [
    "rmse_dtree = np.sqrt(mse_dtree)\n",
    "rmse_bag= np.sqrt(mse_bagging)\n",
    "rmse_voting = np.sqrt(mse_voting)\n",
    "\n",
    "print(f'RMSE Decision tree_ {rmse_dtree}')\n",
    "print(f'RMSE Bagging :{rmse_bag}')\n",
    "print(f'RMSE Voting :{rmse_voting}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "267cc30f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cross validation , cv on Decision tree model "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a544b7d9",
   "metadata": {},
   "source": [
    "## 4. Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "788e452a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the model\n",
    "rf = RandomForestRegressor(n_estimators=500, max_leaf_nodes=16, n_jobs=-1, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "a2470924",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_leaf_nodes=16, n_estimators=500, n_jobs=-1,\n",
       "                      random_state=42)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#training model to Rfor.\n",
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "9c2edeca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions on the test set\n",
    "y_pred = rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "8f2db46b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error: 0.5733075152881297\n",
      "Mean squared error: 0.554279050348407\n",
      "Root means squared error: 0.7444991943235446\n",
      "R2 score: 0.33871203862889143\n"
     ]
    }
   ],
   "source": [
    "# evaluate the model performance using mean absolute error and mean squared error and RMSE\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "rmse = np.sqrt(mse)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "print(f'Mean absolute error: {mae}')\n",
    "print(f'Mean squared error: {mse}')\n",
    "print(f'Root means squared error: {rmse}')\n",
    "print(f'R2 score: {r2}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8973b3d",
   "metadata": {},
   "source": [
    "## 5. K-nearest neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "43309cb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f69effc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor()"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the KNN model\n",
    "k = 5\n",
    "knn = neighbors.KNeighborsRegressor(n_neighbors =k)\n",
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "a4d3dfbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions on the test set\n",
    "y_pred = knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "9ad9347f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error: 0.7409392265193371\n",
      "Mean squared error: 0.9311635359116022\n",
      "Root means squared error: 0.9649681527965585\n",
      "R2 score: -0.1109336280688209\n"
     ]
    }
   ],
   "source": [
    "#Model performance metrics\n",
    "\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "rmse = np.sqrt(mse)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "print(f'Mean absolute error: {mae}')\n",
    "print(f'Mean squared error: {mse}')\n",
    "print(f'Root means squared error: {rmse}')\n",
    "print(f'R2 score: {r2}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "562f9c14",
   "metadata": {},
   "source": [
    "## 6. Neural networks "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1a367c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ask Antonio of recommended"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "68c86519",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "564f3947",
   "metadata": {},
   "source": [
    "# Analysis of different models "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "022ac11f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here we would iterate through different metrics of each model , comparison of performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "9a43b732",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix, plot_confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "8cdc6056",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'dict' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/m7/9cltlp9x46vd9s1599v_nw3m0000gn/T/ipykernel_6031/4220578087.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m                  'KNN': {'MSE': 0.93, 'RMSE': 0.96}}\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mmodels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmodel_scores\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;31m# Create a scatter plot for the RMSE and MSE values of the 3 different models\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: 'dict' object is not callable"
     ]
    }
   ],
   "source": [
    "# Dictionary of trained models with their names as keys\n",
    "models = {\"LinReg\": lr, \"SVR\": svm_reg, \"Decision Tree\": dtree, \"Random Forest\": rf, \"KNN\": knn}\n",
    "\n",
    "model_scores = {'LinReg': {'MSE': 0.56, 'RMSE': 0.75}, \n",
    "                 'SVR': {'MSE': 0.83, 'RMSE': 0.91}, \n",
    "                 'Decision Tree': {'MSE': 0.64, 'RMSE': 0.8}, \n",
    "                 'Random Forest': {'MSE': 0.55, 'RMSE': 0.74}, \n",
    "                 'KNN': {'MSE': 0.93, 'RMSE': 0.96}}\n",
    "\n",
    "for models in model_scores():\n",
    "      \n",
    "# Create a scatter plot for the RMSE and MSE values of the 3 different models\n",
    "    plt(x='MSE', y='RMSE',label='Linear Regression')\n",
    "    plt(x='MSE', y='RMSE',label='SVR')\n",
    "    plt(x='MSE', y='RMSE',label= 'Decision Tree')\n",
    "    plt(x='MSE', y='RMSE',label= 'Random Forest')\n",
    "    plt(x='MSE', y='RMSE',label= 'KNN')\n",
    "    \n",
    "# Display the plots\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b70efbe3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc86db0a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "082caf4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loop through Accuracy scores and plot as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6527787",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Accuracies = {\"Linear reg\": model1, \"svm\": model2, \"knn\": model3....}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d7e7b8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#for name, model in models.items():\n",
    "    #plot_confusion_matrix(model, X_test, y_test, cmap=plt.cm.Blues, normalize='true')\n",
    "    #plt.title(\"Confusion matrix for \" + name)\n",
    "    #plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f8be56f",
   "metadata": {},
   "source": [
    " ### Hypertuning models with GridSearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "360f7a2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Example code to be-edited: \n",
    "\n",
    "param_grid = [\n",
    "{'n_estimators': [3, 10, 30], 'max_features': [2, 4, 6, 8]},\n",
    "{'bootstrap': [False], 'n_estimators': [3, 10], 'max_features': [2, 3, 4]},\n",
    "]\n",
    "\n",
    "fr = RandomForestRegressor()\n",
    "\n",
    "grid_search = GridSearchCV(forest_reg, param_grid, cv=5, scoring='neg_mean_squared_error',\n",
    "return_train_score=True) \n",
    "#grid_search.fit(df, df_labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65716cab",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55437974",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "68cc6f8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Confidence interval \n",
    "confidence = 0.95\n",
    "squared_errors = (final_predictions - y_test) ** 2 \n",
    "np.sqrt(stats.t.interval(confidence, len(squared_errors) - 1, \n",
    "                        loc=squared_errors.mean(), scale=stats.sem(squared_errors)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4dad693",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9aa82129",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bff932df",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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