DiabetesKNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DS 3000 - Assignment 10\n",
    "\n",
    "**Student Name**: Daniel Mccue\n",
    "\n",
    "**Date**: 11/6/24\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 1: \n",
    "\n",
    "load the data directly from the URL into a DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "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>case</th>\n",
       "      <th>bwt</th>\n",
       "      <th>gestation</th>\n",
       "      <th>parity</th>\n",
       "      <th>age</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>smoke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>284.0</td>\n",
       "      <td>0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>113</td>\n",
       "      <td>282.0</td>\n",
       "      <td>0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>128</td>\n",
       "      <td>279.0</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>123</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>108</td>\n",
       "      <td>282.0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   case  bwt  gestation  parity   age  height  weight  smoke\n",
       "0     1  120      284.0       0  27.0    62.0   100.0    0.0\n",
       "1     2  113      282.0       0  33.0    64.0   135.0    0.0\n",
       "2     3  128      279.0       0  28.0    64.0   115.0    1.0\n",
       "3     4  123        NaN       0  36.0    69.0   190.0    0.0\n",
       "4     5  108      282.0       0  23.0    67.0   125.0    1.0"
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "\n",
    "df = pd.read_csv('babies(1).csv')\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 2: \n",
    "10 pts) Data Prep & EDA\n",
    "Prepare the data for analysis. Ensure that you address the following:\n",
    "    * Perform EDA using the dataset to demonstrate patterns.\n",
    "    * Handle any missing data and invalid data. Justify your approach.\n",
    "    * Plot a histogram of the birth weight and explain the overall distribution. \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "case          0\n",
      "bwt           0\n",
      "gestation    13\n",
      "parity        0\n",
      "age           2\n",
      "height       22\n",
      "weight       36\n",
      "smoke        10\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "\n",
    "missing_counts = df.isna().sum()\n",
    "\n",
    "\n",
    "\n",
    "print(missing_counts)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "case         0\n",
      "bwt          0\n",
      "gestation    0\n",
      "parity       0\n",
      "age          0\n",
      "height       0\n",
      "weight       0\n",
      "smoke        0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#Replace NAs with median\n",
    "df.fillna({\n",
    "    'weight': df.weight.median(),\n",
    "    'height': df.height.median(),\n",
    "    'gestation': df.gestation.median(),\n",
    "    'age': df.age.median()\n",
    "}, inplace=True)\n",
    "\n",
    "#Assume no smoke for those missing vals\n",
    "df.fillna({'smoke': 0}, inplace=True)\n",
    "\n",
    "\n",
    "newMissing = df.isna().sum()\n",
    "print(newMissing)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I cleaned the data by replacing all numerical nulls with the median value for that data column, then for the categorical variable of smoke I assume all nulls did not smoke. These choices allow for a complete data set, with relatively little inaccuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Histogram of bwt\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.histplot(data=df, x='bwt')\n",
    "plt.title('Distribution of Birthweights')\n",
    "plt.ylabel('Frequency')\n",
    "plt.xlabel('Baby Weight in Ounces')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph of birthweights shows a clear normal distribution, with the average baby weight appearing to be about 115-120 ounces. There is quite a large range of weights in addition, with the lightest baby weighing just under 60 ounces, and the heaviest in the dataset approaching 180ounces."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 3:\n",
    "(5 pts) Feature Selection\n",
    "Demonstrate which features are useful to build the model. Briefly explain the reason for the features that you selected. Note: You can explore correlations, test hypothesis and/or other techniques. Remember that Data Science relies heavily on experimentation. Therefore, you are encouraged to experiment with your data.\n",
    "    * Tip #1: ensure that you carefully consider each feature and share the pros and cons of your decisions.\n",
    "    * Tip #2: you are predicting the birth weight, therefore it should not be one of your explanatory variables.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Smoke vs bwt stats:\n",
      "\n",
      "T-test results:\n",
      "t-statistic: -8.7083\n",
      "p-value: 0.0000\n"
     ]
    }
   ],
   "source": [
    "# Calculate correlations with birth weight\n",
    "correlations = df.corr()['bwt'].sort_values(ascending=False)\n",
    "\n",
    "# correlation heatmap\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.heatmap(df.corr(), annot=True, cmap='coolwarm', center=0)\n",
    "plt.title('Correlation Matrix')\n",
    "plt.show()\n",
    "\n",
    "#statistical tests for smoke and bwt\n",
    "smokers_bwt = df[df['smoke'] == 1]['bwt']\n",
    "non_smokers_bwt = df[df['smoke'] == 0]['bwt']\n",
    "t_stat, p_value = stats.ttest_ind(smokers_bwt, non_smokers_bwt)\n",
    "print('Smoke vs bwt stats:')\n",
    "print(f\"\\nT-test results:\")\n",
    "print(f\"t-statistic: {t_stat:.4f}\")\n",
    "print(f\"p-value: {p_value:.4f}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I created a heatmap to compare different correlations, hence showing which variables have the most similarities. I will list below the pros and cons, and the effectiveness each feature in predictions. I have also printed important statistical testing on smoke and bwt, which shows an extremely strong correlation.\n",
    "\n",
    "\n",
    "Gestation:\n",
    "Strongest positive correlation with birth weight, \n",
    "longer gestation typically results in higher birth weight, \n",
    "primary predictor\n",
    "\n",
    "Weight (Mother's):\n",
    "Pretty strong positive correlation,  \n",
    "Maternal weight influences baby development, \n",
    "decent predictor\n",
    "\n",
    "Height (Mother's):\n",
    "Pretty strong correlation expected, \n",
    "mother height effects baby weight, \n",
    "decent predictor\n",
    "\n",
    "Smoke:\n",
    "Shows large negative impact on birth weight, \n",
    "Binary categorical variable, \n",
    "Important health indicator, \n",
    "decent predictor\n",
    "\n",
    "Age:\n",
    "May have non-linear relationship with birth weight, \n",
    "ICould capture maternal health aspects\n",
    "\n",
    "Parity:\n",
    "Previous pregnancies can influence birth weight, \n",
    "not too good as predictor\n",
    "\n",
    "Other Features:\n",
    "Case: Just an ID number, no predictive value, \n",
    "BWT: This is our target variable (Tip #2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 4:\n",
    "Data Partitioning\n",
    "(2 pts) Partition the prepared data into train/test sets, ensure that your partitions are reproducible. Encode any categorical variables in your training set. Then, use the appropriate approach to encode your test set.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Typical random state value, reproducability\n",
    "RANDOM_STATE = 42\n",
    "\n",
    "#Separate features and target\n",
    "X = df.drop(['bwt', 'case'], axis=1)  #target and ID column\n",
    "y = df.bwt\n",
    "\n",
    "# Split into train and test sets \n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, \n",
    "    test_size=0.2, \n",
    "    random_state=RANDOM_STATE\n",
    ")\n",
    "\n",
    "#Encode categorical variable (smoke)\n",
    "le = LabelEncoder()\n",
    "X_train.loc[:, 'smoke'] = le.fit_transform(X_train['smoke'])\n",
    "X_test.loc[:, 'smoke'] = le.transform(X_test['smoke'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 5:\n",
    "(5 pts) Using the Random Forest algorithm, build a machine learning model that predicts the birth weight. \n",
    "Think about the following: Are you performing classification or regression? Ensure that you choose the appropriate Random Forest Classifier/Regressor from scikit-learn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Random Forest REGRESSOR since we predicting birth weight -> a continuous value\n",
    "\n",
    "#Initialize regressor\n",
    "rf_model = RandomForestRegressor(\n",
    "    n_estimators=100,\n",
    "    random_state=RANDOM_STATE,\n",
    "    n_jobs=-1\n",
    ")\n",
    "\n",
    "# Train\n",
    "rf_model.fit(X_train, y_train)\n",
    "\n",
    "#predict\n",
    "y_pred = rf_model.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 6:\n",
    "(5 pts) Evaluate the predictions from your model and comment on the results. Ensure that you choose the appropriate metric to evaluate the model and round your calculations to two decimal places. Remember that we evaluate models differently depending on the task, i.e. classification or regression.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Eval Metrics:\n",
      "Root Mean Square Error (RMSE): 17.46\n",
      "Mean Absolute Error (MAE): 13.93\n",
      "R-squared: 0.23\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Calculate various regression variables\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "rmse = np.sqrt(mse)\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "# Round to 2 dec place\n",
    "print(\"Model Eval Metrics:\")\n",
    "print(f\"Root Mean Square Error (RMSE): {rmse:.2f}\")\n",
    "print(f\"Mean Absolute Error (MAE): {mae:.2f}\")\n",
    "print(f\"R-squared: {r2:.2f}\")\n",
    "\n",
    "# Visualize actual vs predicted\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(y_test, y_pred, alpha=0.5)\n",
    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'r--', lw=2)\n",
    "plt.xlabel('Actual Birth Weight')\n",
    "plt.ylabel('Predicted Birth Weight')\n",
    "plt.title('Actual vs Predicted Birth Weight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model for predicting birth weights shows moderate performance with an RMSE of 17.46 and MAE of 13.93, indicating average prediction errors of about 17.46 and 13.93 units. The R-squared value of 0.23 suggests that the model explains only 23% of the variance in birth weights, and the scatter plot shows considerable dispersion around the prediction line, indicating that while there is a slight positive correlation between predicted and actual values, the model's predictive power is relatively limited."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 7:\n",
    "Model Tuning\n",
    "(5 pts) What are your thoughts on the model's prediction capability? Did it do a good job of predicting the birth weight?\n",
    "Note: it is okay if your first version of the model is not the best. However, there are strategies that we can use to improve it, such as tuning.\n",
    "Tune the model: you can control the number of trees in your forest using the n_estimators parameter. Refer to the sample code from the lecture and also view the documentation**Links to an external site. to learn more about this algorithm and its parameters. Try at least five different values for the n_estimators parameter to determine if the model improved. Show all your steps to create, evaluate and tune each version of the model.\n",
    "Indicate which model performed the best.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Performance with Different n_estimators:\n",
      "   n_estimators   rmse    r2\n",
      "0            50  17.60  0.21\n",
      "1           100  17.46  0.23\n",
      "2           200  17.51  0.22\n",
      "3           500  17.53  0.22\n",
      "4          1000  17.50  0.22\n"
     ]
    }
   ],
   "source": [
    "# Try diff numbers of trees or n_estimators\n",
    "n_estimators_list = [50, 100, 200, 500, 1000]\n",
    "results = []\n",
    "\n",
    "for n in n_estimators_list:\n",
    "    rf = RandomForestRegressor(n_estimators=n, random_state=42)\n",
    "    rf.fit(X_train, y_train)\n",
    "    \n",
    "    y_pred = rf.predict(X_test)\n",
    "    \n",
    "    rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
    "    r2 = r2_score(y_test, y_pred)\n",
    "    \n",
    "    results.append({\n",
    "        'n_estimators': n,\n",
    "        'rmse': rmse,\n",
    "        'r2': r2\n",
    "    })\n",
    "\n",
    "# Convert results to df\n",
    "results_df = pd.DataFrame(results)\n",
    "print(\"Model Performance with Different n_estimators:\")\n",
    "print(results_df.round(2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After testing different numbers of estimators, the model with 100 estimators performed slightly better with an RMSE of 17.46 and R-squared of 0.23. However, the differences in performance across different n_estimators were minimal, with RMSE values ranging from 17.46 to 17.60 and R values between 0.21 and 0.23. The minimal variation in performance metrics across different n_estimators suggests that increasing the number of trees beyond 100 does not significantly improve the model's predictive capability. This indicates that the model's limitations likely stem from other factors, such as the factor features or the natural complexity of birth weight prediction, rather than the number of trees in the random forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 8:\n",
    "(15 pts) Perform steps 5-7 using the k-nn algorithm. Perform model tuning using at least 5 different values of k. Show all your steps to create, evaluate and tune each version of the model. Indicate which model performed the best."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "k-NN Model Performance with Different k values:\n",
      "    k   rmse    r2\n",
      "0   3  19.24  0.06\n",
      "1   5  18.45  0.14\n",
      "2   7  17.81  0.20\n",
      "3   9  17.46  0.23\n",
      "4  11  17.28  0.24\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Scale\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# Try diff k values\n",
    "k_values = [3, 5, 7, 9, 11]\n",
    "knn_results = []\n",
    "\n",
    "for k in k_values:\n",
    "    knn = KNeighborsRegressor(n_neighbors=k)\n",
    "    knn.fit(X_train_scaled, y_train)\n",
    "    \n",
    "\n",
    "    y_pred = knn.predict(X_test_scaled)\n",
    "    \n",
    "\n",
    "    rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
    "    r2 = r2_score(y_test, y_pred)\n",
    "    \n",
    "    knn_results.append({\n",
    "        'k': k,\n",
    "        'rmse': rmse,\n",
    "        'r2': r2\n",
    "    })\n",
    "\n",
    "#convert to df \n",
    "knn_results_df = pd.DataFrame(knn_results)\n",
    "print(\"\\nk-NN Model Performance with Different k values:\")\n",
    "print(knn_results_df.round(2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model performance improved consistently as k increased, with k=11 achieving the best results (RMSE=17.28, r2=0.24). This suggests that using more neighbors helps lower variability and inaccuracy in the predictions. The decreasing RMSE values indicate better accuracy, while the increasing R-squared values show improved explanation of variance in the target variable. The improvement in performance from k=3 to k=11 demonstrates that using more neighbors provides better predictions for this birth weight dataset, though the model's overall predictive power remains less than ideal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 9:\n",
    "(3 pts) Compare both the best model from both the Random Forest and k-NN algorithms and explain which model is the most suitable to predict birthweight. Justify your response."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Comparison:\n",
      "Random Forest:\n",
      "RMSE: 17.46\n",
      "R²: 0.23\n",
      "\n",
      "k-NN:\n",
      "RMSE: 17.28\n",
      "R²: 0.24\n"
     ]
    }
   ],
   "source": [
    "# best models from both approaches\n",
    "best_rf = RandomForestRegressor(n_estimators=100, random_state=42)  # use 100, the best\n",
    "best_rf.fit(X_train, y_train)\n",
    "rf_pred = best_rf.predict(X_test)\n",
    "\n",
    "best_knn = KNeighborsRegressor(n_neighbors=11)  #11 neighbors was best\n",
    "best_knn.fit(X_train_scaled, y_train)\n",
    "knn_pred = best_knn.predict(X_test_scaled)\n",
    "\n",
    "# Compare\n",
    "print(\"\\nModel Comparison:\")\n",
    "print(\"Random Forest:\")\n",
    "print(f\"RMSE: {np.sqrt(mean_squared_error(y_test, rf_pred)):.2f}\")\n",
    "print(f\"R²: {r2_score(y_test, rf_pred):.2f}\")\n",
    "\n",
    "print(\"\\nk-NN:\")\n",
    "print(f\"RMSE: {np.sqrt(mean_squared_error(y_test, knn_pred)):.2f}\")\n",
    "print(f\"R²: {r2_score(y_test, knn_pred):.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The k-NN model with k=11 slightly outperforms the Random Forest model (n_estimators=100) for birthweight prediction, with a lower RMSE (17.28 vs 17.46) and higher r2 (0.24 vs 0.23). The k-NN model is more suitable as its better accuracy and simpler approach of making predictions based on similar cases in the training data, though both models show relatively modest predictive power overall, with niethere being extremely accurate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 10:\n",
    "(+5 bonus points) The Random forest has a method that returns the importance of each feature in your model. Review the documentation to learn about feature importance. After which, write the appropriate code that demonstrates which features were the most important when making the predictions. Visualize the feature importance using the best model and explain the results.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Feature Importance Ranking:\n",
      "     feature  importance\n",
      "0  gestation        0.38\n",
      "4     weight        0.22\n",
      "2        age        0.18\n",
      "3     height        0.14\n",
      "5      smoke        0.05\n",
      "1     parity        0.03\n"
     ]
    }
   ],
   "source": [
    "# Get feature importance from best Random Forest model\n",
    "feature_importance = pd.DataFrame({\n",
    "    'feature': X_train.columns,\n",
    "    'importance': best_rf.feature_importances_\n",
    "})\n",
    "\n",
    "# Sorted importance\n",
    "feature_importance = feature_importance.sort_values('importance', ascending=False)\n",
    "\n",
    "# Plot feature importance\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(feature_importance['feature'], feature_importance['importance'])\n",
    "plt.xticks(rotation=45)\n",
    "plt.title('Feature Importance in Random Forest Model')\n",
    "plt.xlabel('Features')\n",
    "plt.ylabel('Importance')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"\\nFeature Importance Ranking:\")\n",
    "print(feature_importance.round(2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The printed stats aswell as the bar chart clearly depict that gestation period seems to be the most vital indicator of baby birth weight, while the amount of past pregnancies seems to be the least effective in predicting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Helpful resources \n",
    "Don't forget to cite websites which helped you solve a problem in a unique way.  You can do this in markdown near the code or with a simple one-line comment inside the code cell, or you can list them below. \n",
    "\n",
    "You do not need to cite the official python documentation.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "- Some stack overflow suggestions on feat importance and how the diff models work, aswell as when to use each. Mainly just explanation of random forest stuff.\n"
   ]
  }
 ],
 "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": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}