decisionscience/trial1

1{2 "cells": [3  {4   "cell_type": "code",5   "execution_count": 1,6   "id": "e2d9e6fa",7   "metadata": {},8   "outputs": [],9   "source": [10    "import seaborn as sns\n",11    "import pandas as pd\n",12    "from sklearn.model_selection import train_test_split\n",13    "from sklearn.linear_model import LinearRegression\n",14    "from sklearn.metrics import mean_squared_error, r2_score\n",15    "\n",16    "# Load dataset\n",17    "df = sns.load_dataset('mpg')\n",18    "df.dropna(inplace=True)  # Dropping missing values"19   ]20  },21  {22   "cell_type": "code",23   "execution_count": 2,24   "id": "6eb9757f",25   "metadata": {},26   "outputs": [],27   "source": [28    "# Selecting relevant features for simplicity\n",29    "features = df[['cylinders', 'displacement', 'horsepower', 'weight', 'acceleration', 'model_year']]\n",30    "target = df['mpg']\n",31    "\n",32    "# Splitting the dataset into training and testing sets\n",33    "X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)"34   ]35  },36  {37   "cell_type": "code",38   "execution_count": 4,39   "id": "72821417",40   "metadata": {},41   "outputs": [],42   "source": [43    "# Create and train the model\n",44    "model = LinearRegression()\n",45    "model.fit(X_train, y_train)\n",46    "\n",47    "# Predictions and Evaluation\n",48    "y_pred = model.predict(X_test)"49   ]50  },51  {52   "cell_type": "code",53   "execution_count": 6,54   "id": "5dc111db",55   "metadata": {},56   "outputs": [57    {58     "name": "stdout",59     "output_type": "stream",60     "text": [61      "Requirement already satisfied: joblib in c:\\users\\user\\anaconda3\\lib\\site-packages (1.2.0)\n"62     ]63    }64   ],65   "source": [66    "#!pip install joblib"67   ]68  },69  {70   "cell_type": "code",71   "execution_count": 7,72   "id": "c41776ae",73   "metadata": {},74   "outputs": [],75   "source": [76    "import joblib"77   ]78  },79  {80   "cell_type": "code",81   "execution_count": 8,82   "id": "318d866d",83   "metadata": {},84   "outputs": [85    {86     "data": {87      "text/plain": [88       "['mpg_model.pkl']"89      ]90     },91     "execution_count": 8,92     "metadata": {},93     "output_type": "execute_result"94    }95   ],96   "source": [97    "# Save the model\n",98    "joblib.dump(model, 'mpg_model.pkl')"99   ]100  },101  {102   "cell_type": "code",103   "execution_count": null,104   "id": "7636f0d3",105   "metadata": {},106   "outputs": [],107   "source": []108  }109 ],110 "metadata": {111  "kernelspec": {112   "display_name": "Python 3 (ipykernel)",113   "language": "python",114   "name": "python3"115  },116  "language_info": {117   "codemirror_mode": {118    "name": "ipython",119    "version": 3120   },121   "file_extension": ".py",122   "mimetype": "text/x-python",123   "name": "python",124   "nbconvert_exporter": "python",125   "pygments_lexer": "ipython3",126   "version": "3.11.5"127  }128 },129 "nbformat": 4,130 "nbformat_minor": 5131}132