{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b14d650c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import required libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
    "from sklearn.metrics import r2_score, mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a4e71c7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(200000, 9)\n"
     ]
    },
    {
     "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>Unnamed: 0</th>\n",
       "      <th>key</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24238194</td>\n",
       "      <td>2015-05-07 19:52:06.0000003</td>\n",
       "      <td>7.5</td>\n",
       "      <td>2015-05-07 19:52:06 UTC</td>\n",
       "      <td>-73.999817</td>\n",
       "      <td>40.738354</td>\n",
       "      <td>-73.999512</td>\n",
       "      <td>40.723217</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27835199</td>\n",
       "      <td>2009-07-17 20:04:56.0000002</td>\n",
       "      <td>7.7</td>\n",
       "      <td>2009-07-17 20:04:56 UTC</td>\n",
       "      <td>-73.994355</td>\n",
       "      <td>40.728225</td>\n",
       "      <td>-73.994710</td>\n",
       "      <td>40.750325</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44984355</td>\n",
       "      <td>2009-08-24 21:45:00.00000061</td>\n",
       "      <td>12.9</td>\n",
       "      <td>2009-08-24 21:45:00 UTC</td>\n",
       "      <td>-74.005043</td>\n",
       "      <td>40.740770</td>\n",
       "      <td>-73.962565</td>\n",
       "      <td>40.772647</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25894730</td>\n",
       "      <td>2009-06-26 08:22:21.0000001</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2009-06-26 08:22:21 UTC</td>\n",
       "      <td>-73.976124</td>\n",
       "      <td>40.790844</td>\n",
       "      <td>-73.965316</td>\n",
       "      <td>40.803349</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17610152</td>\n",
       "      <td>2014-08-28 17:47:00.000000188</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2014-08-28 17:47:00 UTC</td>\n",
       "      <td>-73.925023</td>\n",
       "      <td>40.744085</td>\n",
       "      <td>-73.973082</td>\n",
       "      <td>40.761247</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                            key  fare_amount  \\\n",
       "0    24238194    2015-05-07 19:52:06.0000003          7.5   \n",
       "1    27835199    2009-07-17 20:04:56.0000002          7.7   \n",
       "2    44984355   2009-08-24 21:45:00.00000061         12.9   \n",
       "3    25894730    2009-06-26 08:22:21.0000001          5.3   \n",
       "4    17610152  2014-08-28 17:47:00.000000188         16.0   \n",
       "\n",
       "           pickup_datetime  pickup_longitude  pickup_latitude  \\\n",
       "0  2015-05-07 19:52:06 UTC        -73.999817        40.738354   \n",
       "1  2009-07-17 20:04:56 UTC        -73.994355        40.728225   \n",
       "2  2009-08-24 21:45:00 UTC        -74.005043        40.740770   \n",
       "3  2009-06-26 08:22:21 UTC        -73.976124        40.790844   \n",
       "4  2014-08-28 17:47:00 UTC        -73.925023        40.744085   \n",
       "\n",
       "   dropoff_longitude  dropoff_latitude  passenger_count  \n",
       "0         -73.999512         40.723217                1  \n",
       "1         -73.994710         40.750325                1  \n",
       "2         -73.962565         40.772647                1  \n",
       "3         -73.965316         40.803349                3  \n",
       "4         -73.973082         40.761247                5  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the Uber fares dataset\n",
    "data = pd.read_csv(\"uber.csv\")\n",
    "print(data.shape)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e2c1a5b5",
   "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>fare_amount</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.5</td>\n",
       "      <td>2015-05-07 19:52:06+00:00</td>\n",
       "      <td>-73.999817</td>\n",
       "      <td>40.738354</td>\n",
       "      <td>-73.999512</td>\n",
       "      <td>40.723217</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2009-07-17 20:04:56+00:00</td>\n",
       "      <td>-73.994355</td>\n",
       "      <td>40.728225</td>\n",
       "      <td>-73.994710</td>\n",
       "      <td>40.750325</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12.9</td>\n",
       "      <td>2009-08-24 21:45:00+00:00</td>\n",
       "      <td>-74.005043</td>\n",
       "      <td>40.740770</td>\n",
       "      <td>-73.962565</td>\n",
       "      <td>40.772647</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.3</td>\n",
       "      <td>2009-06-26 08:22:21+00:00</td>\n",
       "      <td>-73.976124</td>\n",
       "      <td>40.790844</td>\n",
       "      <td>-73.965316</td>\n",
       "      <td>40.803349</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16.0</td>\n",
       "      <td>2014-08-28 17:47:00+00:00</td>\n",
       "      <td>-73.925023</td>\n",
       "      <td>40.744085</td>\n",
       "      <td>-73.973082</td>\n",
       "      <td>40.761247</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fare_amount           pickup_datetime  pickup_longitude  pickup_latitude  \\\n",
       "0          7.5 2015-05-07 19:52:06+00:00        -73.999817        40.738354   \n",
       "1          7.7 2009-07-17 20:04:56+00:00        -73.994355        40.728225   \n",
       "2         12.9 2009-08-24 21:45:00+00:00        -74.005043        40.740770   \n",
       "3          5.3 2009-06-26 08:22:21+00:00        -73.976124        40.790844   \n",
       "4         16.0 2014-08-28 17:47:00+00:00        -73.925023        40.744085   \n",
       "\n",
       "   dropoff_longitude  dropoff_latitude  passenger_count  \n",
       "0         -73.999512         40.723217                1  \n",
       "1         -73.994710         40.750325                1  \n",
       "2         -73.962565         40.772647                1  \n",
       "3         -73.965316         40.803349                3  \n",
       "4         -73.973082         40.761247                5  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove unnecessary columns and prepare the dataset\n",
    "data = data.drop(columns=[\"key\", \"Unnamed: 0\"], errors=\"ignore\")\n",
    "data[\"pickup_datetime\"] = pd.to_datetime(data[\"pickup_datetime\"], errors=\"coerce\", utc=True)\n",
    "data = data.dropna(subset=[\n",
    "    \"fare_amount\", \"pickup_datetime\", \"pickup_longitude\", \"pickup_latitude\",\n",
    "    \"dropoff_longitude\", \"dropoff_latitude\", \"passenger_count\"\n",
    "])\n",
    "\n",
    "data = data[\n",
    "    (data[\"fare_amount\"] > 0) &\n",
    "    (data[\"pickup_longitude\"].between(-180, 180)) &\n",
    "    (data[\"dropoff_longitude\"].between(-180, 180)) &\n",
    "    (data[\"pickup_latitude\"].between(-90, 90)) &\n",
    "    (data[\"dropoff_latitude\"].between(-90, 90)) &\n",
    "    (data[\"pickup_longitude\"] != 0) &\n",
    "    (data[\"pickup_latitude\"] != 0) &\n",
    "    (data[\"dropoff_longitude\"] != 0) &\n",
    "    (data[\"dropoff_latitude\"] != 0) &\n",
    "    (data[\"passenger_count\"].between(1, 6))\n",
    "].copy()\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "771bfdfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create date and time features\n",
    "data[\"pickup_hour\"] = data[\"pickup_datetime\"].dt.hour\n",
    "data[\"pickup_day\"] = data[\"pickup_datetime\"].dt.day\n",
    "data[\"pickup_month\"] = data[\"pickup_datetime\"].dt.month\n",
    "data[\"pickup_weekday\"] = data[\"pickup_datetime\"].dt.dayofweek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "26aee6be",
   "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>fare_amount</th>\n",
       "      <th>distance_km</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.5</td>\n",
       "      <td>1.683323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.457590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12.9</td>\n",
       "      <td>5.036377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.3</td>\n",
       "      <td>1.661683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16.0</td>\n",
       "      <td>4.475450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fare_amount  distance_km\n",
       "0          7.5     1.683323\n",
       "1          7.7     2.457590\n",
       "2         12.9     5.036377\n",
       "3          5.3     1.661683\n",
       "4         16.0     4.475450"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate trip distance using the Haversine formula\n",
    "lat1 = np.radians(data[\"pickup_latitude\"])\n",
    "lat2 = np.radians(data[\"dropoff_latitude\"])\n",
    "lon1 = np.radians(data[\"pickup_longitude\"])\n",
    "lon2 = np.radians(data[\"dropoff_longitude\"])\n",
    "\n",
    "dlat = lat2 - lat1\n",
    "dlon = lon2 - lon1\n",
    "a = np.sin(dlat / 2) ** 2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon / 2) ** 2\n",
    "data[\"distance_km\"] = 6371 * 2 * np.arcsin(np.sqrt(a))\n",
    "data = data[data[\"distance_km\"] > 0].copy()\n",
    "\n",
    "data[[\"fare_amount\", \"distance_km\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ac27f13e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fare outliers: 16548\n",
      "Distance outliers: 16089\n",
      "Rows before removing outliers: 193270\n",
      "Rows after removing outliers: 173750\n"
     ]
    }
   ],
   "source": [
    "# Identify outliers using the IQR method\n",
    "def get_iqr_bounds(series):\n",
    "    q1 = series.quantile(0.25)\n",
    "    q3 = series.quantile(0.75)\n",
    "    iqr = q3 - q1\n",
    "    return q1 - 1.5 * iqr, q3 + 1.5 * iqr\n",
    "\n",
    "fare_low, fare_high = get_iqr_bounds(data[\"fare_amount\"])\n",
    "distance_low, distance_high = get_iqr_bounds(data[\"distance_km\"])\n",
    "\n",
    "fare_outliers = ~data[\"fare_amount\"].between(fare_low, fare_high)\n",
    "distance_outliers = ~data[\"distance_km\"].between(distance_low, distance_high)\n",
    "\n",
    "print(\"Fare outliers:\", fare_outliers.sum())\n",
    "print(\"Distance outliers:\", distance_outliers.sum())\n",
    "print(\"Rows before removing outliers:\", len(data))\n",
    "\n",
    "data = data[\n",
    "    data[\"fare_amount\"].between(fare_low, fare_high) &\n",
    "    data[\"distance_km\"].between(distance_low, distance_high)\n",
    "].copy()\n",
    "\n",
    "print(\"Rows after removing outliers:\", len(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "26235f42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fare_amount          1.000000\n",
      "distance_km          0.817186\n",
      "pickup_month         0.029480\n",
      "pickup_weekday       0.017867\n",
      "passenger_count      0.015634\n",
      "pickup_day           0.006156\n",
      "dropoff_longitude    0.002916\n",
      "pickup_longitude     0.002295\n",
      "pickup_hour         -0.002843\n",
      "pickup_latitude     -0.003219\n",
      "dropoff_latitude    -0.003584\n",
      "Name: fare_amount, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check feature correlations\n",
    "features = [\n",
    "    \"pickup_longitude\", \"pickup_latitude\", \"dropoff_longitude\",\n",
    "    \"dropoff_latitude\", \"passenger_count\", \"pickup_hour\", \"pickup_day\",\n",
    "    \"pickup_month\", \"pickup_weekday\", \"distance_km\"\n",
    "]\n",
    "correlation = data[features + [\"fare_amount\"]].corr()\n",
    "print(correlation[\"fare_amount\"].sort_values(ascending=False))\n",
    "\n",
    "plt.figure(figsize=(10, 7))\n",
    "plt.imshow(correlation, cmap=\"coolwarm\", vmin=-1, vmax=1)\n",
    "plt.colorbar(label=\"Correlation\")\n",
    "plt.xticks(range(len(correlation.columns)), correlation.columns, rotation=90)\n",
    "plt.yticks(range(len(correlation.columns)), correlation.columns)\n",
    "plt.title(\"Correlation Heatmap\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f0ffb8fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into input features and target\n",
    "X = data[features]\n",
    "y = data[\"fare_amount\"]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.2, random_state=42\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c5531d17",
   "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>Model</th>\n",
       "      <th>R2 Score</th>\n",
       "      <th>RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>0.661123</td>\n",
       "      <td>2.306202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ridge Regression</td>\n",
       "      <td>0.661095</td>\n",
       "      <td>2.306297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lasso Regression</td>\n",
       "      <td>0.657524</td>\n",
       "      <td>2.318413</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Model  R2 Score      RMSE\n",
       "0  Linear Regression  0.661123  2.306202\n",
       "1   Ridge Regression  0.661095  2.306297\n",
       "2   Lasso Regression  0.657524  2.318413"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train linear regression, Ridge, and Lasso models\n",
    "models = {\n",
    "    \"Linear Regression\": make_pipeline(StandardScaler(), LinearRegression()),\n",
    "    \"Ridge Regression\": make_pipeline(StandardScaler(), Ridge(alpha=1.0)),\n",
    "    \"Lasso Regression\": make_pipeline(StandardScaler(), Lasso(alpha=0.1, max_iter=10000))\n",
    "}\n",
    "\n",
    "results = []\n",
    "for name, model in models.items():\n",
    "    model.fit(X_train, y_train)\n",
    "    predictions = model.predict(X_test)\n",
    "    results.append({\n",
    "        \"Model\": name,\n",
    "        \"R2 Score\": r2_score(y_test, predictions),\n",
    "        \"RMSE\": np.sqrt(mean_squared_error(y_test, predictions))\n",
    "    })\n",
    "\n",
    "results_df = pd.DataFrame(results)\n",
    "results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4e8ec9d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Model  R2 Score     RMSE\n",
      "Linear Regression  0.661123 2.306202\n",
      " Ridge Regression  0.661095 2.306297\n",
      " Lasso Regression  0.657524 2.318413\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare model R2 scores and RMSE values\n",
    "print(results_df.sort_values(\"R2 Score\", ascending=False).to_string(index=False))\n",
    "\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.bar(results_df[\"Model\"], results_df[\"R2 Score\"])\n",
    "plt.ylabel(\"R2 Score\")\n",
    "plt.title(\"Regression Model Comparison\")\n",
    "plt.xticks(rotation=15)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.bar(results_df[\"Model\"], results_df[\"RMSE\"])\n",
    "plt.ylabel(\"RMSE\")\n",
    "plt.title(\"RMSE Comparison\")\n",
    "plt.xticks(rotation=15)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}