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app.py

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "728431f5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "fd56baf1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2023-12-25 15:31:55.354 \n",
+      "  \u001b[33m\u001b[1mWarning:\u001b[0m to view this Streamlit app on a browser, run it with the following\n",
+      "  command:\n",
+      "\n",
+      "    streamlit run C:\\Users\\user\\anaconda3\\Lib\\site-packages\\ipykernel_launcher.py [ARGUMENTS]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import streamlit as st\n",
+    "import pandas as pd\n",
+    "import joblib\n",
+    "\n",
+    "# Load trained model\n",
+    "model = joblib.load('mpg_model.pkl')  # Ensure this path is correct\n",
+    "\n",
+    "def user_input_features():\n",
+    "    cylinders = st.sidebar.slider('Cylinders', 3, 8, 4)\n",
+    "    displacement = st.sidebar.number_input('Displacement')\n",
+    "    horsepower = st.sidebar.number_input('Horsepower')\n",
+    "    weight = st.sidebar.number_input('Weight')\n",
+    "    acceleration = st.sidebar.number_input('Acceleration')\n",
+    "    model_year = st.sidebar.slider('Model Year', 70, 82, 76)\n",
+    "    data = {'cylinders': cylinders,\n",
+    "            'displacement': displacement,\n",
+    "            'horsepower': horsepower,\n",
+    "            'weight': weight,\n",
+    "            'acceleration': acceleration,\n",
+    "            'model_year': model_year}\n",
+    "    features = pd.DataFrame(data, index=[0])\n",
+    "    return features\n",
+    "\n",
+    "# Main Streamlit app interface\n",
+    "st.write(\"\"\"\n",
+    "# Simple MPG Prediction App\n",
+    "This app predicts the **Miles Per Gallon (MPG)** of your car!\n",
+    "\"\"\")\n",
+    "\n",
+    "# User input features\n",
+    "input_df = user_input_features()\n",
+    "\n",
+    "# Display the user input features\n",
+    "st.subheader('User Input features')\n",
+    "st.write(input_df)\n",
+    "\n",
+    "# Predict and display the output\n",
+    "st.subheader('Prediction')\n",
+    "prediction = model.predict(input_df)\n",
+    "st.write(f'Predicted MPG: {prediction[0]:.2f}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f8836f1f",
+   "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": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

model.py

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e2d9e6fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sns\n",
+    "import pandas as pd\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "\n",
+    "# Load dataset\n",
+    "df = sns.load_dataset('mpg')\n",
+    "df.dropna(inplace=True)  # Dropping missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "6eb9757f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Selecting relevant features for simplicity\n",
+    "features = df[['cylinders', 'displacement', 'horsepower', 'weight', 'acceleration', 'model_year']]\n",
+    "target = df['mpg']\n",
+    "\n",
+    "# Splitting the dataset into training and testing sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "72821417",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create and train the model\n",
+    "model = LinearRegression()\n",
+    "model.fit(X_train, y_train)\n",
+    "\n",
+    "# Predictions and Evaluation\n",
+    "y_pred = model.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "5dc111db",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: joblib in c:\\users\\user\\anaconda3\\lib\\site-packages (1.2.0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#!pip install joblib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c41776ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import joblib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "318d866d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['mpg_model.pkl']"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Save the model\n",
+    "joblib.dump(model, 'mpg_model.pkl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7636f0d3",
+   "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": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

mpg_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3eda59e103b0644df6fd16650bd9da8556b44235c569eef8dd0dcc6dac8213f0
+size 1032