Beer Consumption Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f6cc42cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression, Lasso, Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "aa8cdf71",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Consumo_cerveja.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c75cf5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "be336c0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(941, 7)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "adee7e4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = [\"Data\", \"Medium Temperature\", \"Minimum Temperature\",\n",
    "              \"Maximum Temperature\", \"Precipitacao\", \"End of Week\",\n",
    "              \"Beer Consumption\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4cb0460",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "696a849b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "67a9e427",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(how=\"all\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7910e34e",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = df.columns[1:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "69ff2685",
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in cols:\n",
    "    df[col] = df[col].str.replace(\",\", \".\")\n",
    "    df[col] = df[col].apply(pd.to_numeric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "88093692",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(365, 7)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "42231e1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Data\"] = pd.to_datetime(df[\"Data\"])\n",
    "df[\"Day\"] = df[\"Data\"].dt.day\n",
    "df[\"Month\"] = df[\"Data\"].dt.month\n",
    "df[\"Year\"] = df[\"Data\"].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "83fbb8e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(\"Data\", axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9867f894",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[[\"Day\", \"Month\", \"Year\", \"Medium Temperature\", \"Minimum Temperature\",\n",
    "         \"Maximum Temperature\", \"Precipitacao\", \"End of Week\", \"Beer Consumption\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ccc507af",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c900d3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.lineplot(x=\"Medium Temperature\", y=\"Beer Consumption\", data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7034e0c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.lineplot(x=\"Precipitacao\", y=\"Beer Consumption\", data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "52c406a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year\n",
       "2015    365\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Year\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8269a9f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.barplot(x='Day',y=\"Beer Consumption\",data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "dac64891",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop([\"Day\", \"Month\", \"Year\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fd58eba4",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(\"Beer Consumption\", axis=1)\n",
    "y = df[\"Beer Consumption\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5c694146",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=4242)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d6ff5850",
   "metadata": {},
   "outputs": [],
   "source": [
    "linreg = LinearRegression()\n",
    "linreg.fit(X_train, y_train)\n",
    "y_pred = linreg.predict(X_test)\n",
    "ac1 = r2_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b300a253",
   "metadata": {},
   "outputs": [],
   "source": [
    "lasso = Lasso()\n",
    "lasso.fit(X_train, y_train)\n",
    "y_pred = lasso.predict(X_test)\n",
    "ac2 = r2_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "91e1528a",
   "metadata": {},
   "outputs": [],
   "source": [
    "ridge = Ridge()\n",
    "ridge.fit(X_train, y_train)\n",
    "y_pred = ridge.predict(X_test)\n",
    "ac3 = r2_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "bb54be13",
   "metadata": {},
   "outputs": [],
   "source": [
    "names = [\"Linear Regressino\", \"Lasso\", \"Ridge\"]\n",
    "scores = [ac1, ac2, ac3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "78742f2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>Model</th>\n",
       "      <th>R2 Scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Linear Regressino</td>\n",
       "      <td>0.731133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lasso</td>\n",
       "      <td>0.377432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ridge</td>\n",
       "      <td>0.730447</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Model  R2 Scores\n",
       "0  Linear Regressino   0.731133\n",
       "1              Lasso   0.377432\n",
       "2              Ridge   0.730447"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models = pd.DataFrame({\"Model\": names, \"R2 Scores\": scores})\n",
    "models.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f2094f31",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(linreg, open(\"linreg.pkl\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c217c121",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Model', ylabel='R2 Scores'>"
      ]
     },
     "execution_count": 52,
     "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": [
    "sns.barplot(x=\"Model\", y=\"R2 Scores\", data=models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ddc76d7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}