01_eda_and_poc.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA and POC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "# Data splitting\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Models\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.svm import SVR\n",
    "\n",
    "# Metrics\n",
    "import sklearn.metrics as metrics\n",
    "from sklearn.metrics import f1_score, accuracy_score\n",
    "\n",
    "# Creating a pipeline\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "# GridSearch\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from datetime import datetime\n",
    "from IPython.core.display import display, HTML\n",
    "import requests\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.model_selection import train_test_split, KFold, cross_val_score\n",
    "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n",
    "import scipy.stats as stats\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.preprocessing import StandardScaler,MinMaxScaler\n",
    "import pickle\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plots text color\n",
    "\n",
    "params = {\"ytick.color\" : \"w\",\n",
    "          \"xtick.color\" : \"w\",\n",
    "          \"axes.labelcolor\" : \"w\",\n",
    "          \"axes.edgecolor\" : \"w\"}\n",
    "plt.rcParams.update(params)\n",
    "\n",
    "text_color = 'white'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('./data/movies_semi_cleaned.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Title</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Runtime</th>\n",
       "      <th>Directors</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Income</th>\n",
       "      <th>Action</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>...</th>\n",
       "      <th>Sci-Fi</th>\n",
       "      <th>Sport</th>\n",
       "      <th>Thriller</th>\n",
       "      <th>War</th>\n",
       "      <th>Western</th>\n",
       "      <th>Top_50_Director</th>\n",
       "      <th>Lead</th>\n",
       "      <th>Supporting</th>\n",
       "      <th>Lead_Top</th>\n",
       "      <th>Supporting_Top</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avatar: The Way of Water</td>\n",
       "      <td>7.8</td>\n",
       "      <td>2022</td>\n",
       "      <td>12</td>\n",
       "      <td>192.0</td>\n",
       "      <td>James Cameron</td>\n",
       "      <td>350000000.0</td>\n",
       "      <td>2.267947e+09</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Sam Worthington</td>\n",
       "      <td>Zoe Saldana</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Guillermo del Toro's Pinocchio</td>\n",
       "      <td>7.6</td>\n",
       "      <td>2022</td>\n",
       "      <td>12</td>\n",
       "      <td>117.0</td>\n",
       "      <td>Guillermo del Toro</td>\n",
       "      <td>35000000.0</td>\n",
       "      <td>1.089670e+05</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Ewan McGregor</td>\n",
       "      <td>David Bradley</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Title  Rating  Year Month  Runtime  \\\n",
       "0        Avatar: The Way of Water     7.8  2022    12    192.0   \n",
       "1  Guillermo del Toro's Pinocchio     7.6  2022    12    117.0   \n",
       "\n",
       "            Directors       Budget        Income  Action  Adventure  ...  \\\n",
       "0       James Cameron  350000000.0  2.267947e+09       1          1  ...   \n",
       "1  Guillermo del Toro   35000000.0  1.089670e+05       0          0  ...   \n",
       "\n",
       "   Sci-Fi  Sport  Thriller  War  Western  Top_50_Director             Lead  \\\n",
       "0       0      0         0    0        0                1  Sam Worthington   \n",
       "1       0      0         0    0        0                1    Ewan McGregor   \n",
       "\n",
       "       Supporting  Lead_Top  Supporting_Top  \n",
       "0     Zoe Saldana       1.0             0.0  \n",
       "1   David Bradley       1.0             0.0  \n",
       "\n",
       "[2 rows x 33 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dataset contains 33 columns and 1641 rows\n"
     ]
    }
   ],
   "source": [
    "print(f'The dataset contains {data.shape[1]} columns and {data.shape[0]} rows')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Correaltion Matrix for All Variables')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,10))\n",
    "corr_matrix = data.corr()\n",
    "sns.heatmap(corr_matrix, cmap='coolwarm')\n",
    "plt.title('Correaltion Matrix for All Variables', color=text_color, fontsize=20, pad=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1. Ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,7))\n",
    "\n",
    "# Plot\n",
    "sns.distplot(data['Rating'], bins=30, kde=True)\n",
    "plt.suptitle('Distribution of Movie Ratings', fontsize=20, color=text_color)\n",
    "ax.set_xlabel('Rating', fontsize=15)\n",
    "ax.set_ylabel('Density', fontsize=15)\n",
    "\n",
    "# Table\n",
    "col_labels = ['Most Common Scores']\n",
    "row_labels = ['1', '2', '3', '4', '5']\n",
    "table_vals = [[6.6],[7.1],[7.3],[7.0],[6.5]]\n",
    "table = ax.table(cellText=table_vals,\n",
    "          colLabels=col_labels,\n",
    "          rowLabels=row_labels,\n",
    "          loc='right')\n",
    "\n",
    "table.auto_set_font_size(False)\n",
    "table.set_fontsize(14)\n",
    "table.scale(0.3, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sns.pairplot(data=data, vars=['Rating', 'Year', 'Budget', 'Income', 'Top_50_Director', 'Lead', 'Supporting'])\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2. Genre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Genres Correlation Matrix')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(20,8))\n",
    "\n",
    "# Selecting the Genre columns\n",
    "genre_columns = data.columns[7:27]\n",
    "df_pie = data[genre_columns]\n",
    "\n",
    "# Pie chart for the genres\n",
    "sums = df_pie.sum()\n",
    "ax1.pie(sums, labels=sums.index,textprops={'color': text_color})\n",
    "ax1.set_title('Genres Pie Chart', color=text_color, fontsize=20)\n",
    "\n",
    "# Correlation matrix for the genres\n",
    "corr_matrix = df_pie.corr()\n",
    "sns.heatmap(corr_matrix, cmap='coolwarm', ax=ax2)\n",
    "ax2.set_title('Genres Correlation Matrix', color=text_color, fontsize=20, pad=20)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3. Finances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11f7a308>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 3000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(30,10))\n",
    "\n",
    "# Pie chart for the genres\n",
    "ax.bar(data['Title'], data['Income'], label='Profit')\n",
    "ax.bar(data['Title'], data['Budget'], label='Budget')\n",
    "ax.set_title('Budget vs. Profit', color=text_color, fontsize=30, pad=20)\n",
    "ax.legend(loc='upper right', fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                          Title        Profit\n",
      "993                                      Avatar  2.685918e+09\n",
      "135                           Avengers: Endgame  2.441501e+09\n",
      "0                      Avatar: The Way of Water  1.917947e+09\n",
      "458  Star Wars: Episode VII - The Force Awakens  1.824522e+09\n",
      "209                      Avengers: Infinity War  1.727360e+09\n",
      "50                      Spider-Man: No Way Home  1.717430e+09\n",
      "467                              Jurassic World  1.521537e+09\n",
      "167                               The Lion King  1.403250e+09\n",
      "494                                   Furious 7  1.325341e+09\n",
      "20                            Top Gun: Maverick  1.318733e+09\n"
     ]
    }
   ],
   "source": [
    "# Movies that made the highest profit\n",
    "data['Profit'] = data['Income'] - data['Budget']\n",
    "data_profit = data.sort_values('Profit', ascending=False)\n",
    "print(data_profit[['Title', 'Profit']].head(10))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Correaltion Matrix for All Variables')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,10))\n",
    "corr_matrix = data.corr()\n",
    "sns.heatmap(corr_matrix, cmap='coolwarm')\n",
    "plt.title('Correaltion Matrix for All Variables', color=text_color, fontsize=20, pad=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1641 entries, 0 to 1640\n",
      "Data columns (total 34 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Title            1641 non-null   object \n",
      " 1   Rating           1641 non-null   float64\n",
      " 2   Year             1641 non-null   int64  \n",
      " 3   Month            1641 non-null   object \n",
      " 4   Runtime          1641 non-null   float64\n",
      " 5   Directors        1641 non-null   object \n",
      " 6   Budget           1641 non-null   float64\n",
      " 7   Income           1641 non-null   float64\n",
      " 8   Action           1641 non-null   int64  \n",
      " 9   Adventure        1641 non-null   int64  \n",
      " 10  Animation        1641 non-null   int64  \n",
      " 11  Biography        1641 non-null   int64  \n",
      " 12  Comedy           1641 non-null   int64  \n",
      " 13  Crime            1641 non-null   int64  \n",
      " 14  Drama            1641 non-null   int64  \n",
      " 15  Family           1641 non-null   int64  \n",
      " 16  Fantasy          1641 non-null   int64  \n",
      " 17  History          1641 non-null   int64  \n",
      " 18  Horror           1641 non-null   int64  \n",
      " 19  Music            1641 non-null   int64  \n",
      " 20  Musical          1641 non-null   int64  \n",
      " 21  Mystery          1641 non-null   int64  \n",
      " 22  Romance          1641 non-null   int64  \n",
      " 23  Sci-Fi           1641 non-null   int64  \n",
      " 24  Sport            1641 non-null   int64  \n",
      " 25  Thriller         1641 non-null   int64  \n",
      " 26  War              1641 non-null   int64  \n",
      " 27  Western          1641 non-null   object \n",
      " 28  Top_50_Director  1641 non-null   object \n",
      " 29  Lead             1641 non-null   object \n",
      " 30  Supporting       1641 non-null   object \n",
      " 31  Lead_Top         1640 non-null   float64\n",
      " 32  Supporting_Top   1640 non-null   float64\n",
      " 33  Profit           1641 non-null   float64\n",
      "dtypes: float64(7), int64(20), object(7)\n",
      "memory usage: 436.0+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. POC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop non numeric \n",
    "data = data.select_dtypes(include=[np.number])\n",
    "\n",
    "# Drop rows with NaN values\n",
    "data = data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1640 entries, 0 to 1640\n",
      "Data columns (total 27 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Rating          1640 non-null   float64\n",
      " 1   Year            1640 non-null   int64  \n",
      " 2   Runtime         1640 non-null   float64\n",
      " 3   Budget          1640 non-null   float64\n",
      " 4   Income          1640 non-null   float64\n",
      " 5   Action          1640 non-null   int64  \n",
      " 6   Adventure       1640 non-null   int64  \n",
      " 7   Animation       1640 non-null   int64  \n",
      " 8   Biography       1640 non-null   int64  \n",
      " 9   Comedy          1640 non-null   int64  \n",
      " 10  Crime           1640 non-null   int64  \n",
      " 11  Drama           1640 non-null   int64  \n",
      " 12  Family          1640 non-null   int64  \n",
      " 13  Fantasy         1640 non-null   int64  \n",
      " 14  History         1640 non-null   int64  \n",
      " 15  Horror          1640 non-null   int64  \n",
      " 16  Music           1640 non-null   int64  \n",
      " 17  Musical         1640 non-null   int64  \n",
      " 18  Mystery         1640 non-null   int64  \n",
      " 19  Romance         1640 non-null   int64  \n",
      " 20  Sci-Fi          1640 non-null   int64  \n",
      " 21  Sport           1640 non-null   int64  \n",
      " 22  Thriller        1640 non-null   int64  \n",
      " 23  War             1640 non-null   int64  \n",
      " 24  Lead_Top        1640 non-null   float64\n",
      " 25  Supporting_Top  1640 non-null   float64\n",
      " 26  Profit          1640 non-null   float64\n",
      "dtypes: float64(7), int64(20)\n",
      "memory usage: 358.8 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Specify col to predict\n",
    "target_col = \"Rating\"\n",
    "\n",
    "#Split data into X and y\n",
    "X = data.drop(target_col, axis=1).values\n",
    "y = data[target_col].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Defining features and targets\n",
    "\n",
    "#features = [ \n",
    "#    'Year', \n",
    "#    'Month', \n",
    "#    'Runtime',\n",
    "#    'Budget',\n",
    "#    'Income'] + list(data.columns[7:27])\n",
    "\n",
    "#target = 'Rating'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. Splitting the data\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": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3.1. Training a linear regression model\n",
    "model_lr = LinearRegression()\n",
    "model_lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(random_state=42)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3.2.\n",
    "model_rfr = RandomForestRegressor(n_estimators=100, random_state=42)\n",
    "model_rfr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(random_state=42)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_dt = DecisionTreeRegressor(random_state=42)\n",
    "model_dt.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR()"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_svr = SVR(kernel='rbf')\n",
    "model_svr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4. Predicting the income on the test set\n",
    "y_pred_lr = model_lr.predict(X_test)\n",
    "y_pred_rfr = model_rfr.predict(X_test)\n",
    "y_pred_dt = model_dt.predict(X_test)\n",
    "y_pred_svr = model_svr.predict(X_test)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 0.5988919637354053\n"
     ]
    }
   ],
   "source": [
    "# 5. Calculating the mean squared error of the predictions\n",
    "mse_lr = mean_squared_error(y_test, y_pred_lr)\n",
    "print(f'Mean squared error: {mse_lr}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 0.5749193048780488\n"
     ]
    }
   ],
   "source": [
    "# 5. Calculating the mean squared error of the predictions\n",
    "mse_rfr = mean_squared_error(y_test, y_pred_rfr)\n",
    "print(f'Mean squared error: {mse_rfr}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 1.0096036585365853\n"
     ]
    }
   ],
   "source": [
    "# 5. Calculating the mean squared error of the predictions\n",
    "mse_dt = mean_squared_error(y_test, y_pred_dt)\n",
    "print(f'Mean squared error: {mse_dt}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 0.7762301661827175\n"
     ]
    }
   ],
   "source": [
    "# 5. Calculating the mean squared error of the predictions\n",
    "mse_svr = mean_squared_error(y_test, y_pred_svr)\n",
    "print(f'Mean squared error: {mse_svr}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#f1 = f1_score(y_true, y_pred)\n",
    "#accuracy = accuracy_score(y_true, y_pred)\n",
    "\n",
    "#print(f\"F1 score: {f1:.3f}\")\n",
    "#print(f\"Accuracy: {accuracy:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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