diff --git a/Maternal Health Risk Prediction/maternalHealthRiskPrediction_FeatureImportance_.ipynb b/Maternal Health Risk Prediction/maternalHealthRiskPrediction_FeatureImportance_.ipynb
new file mode 100644
index 0000000..8c674d4
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+++ b/Maternal Health Risk Prediction/maternalHealthRiskPrediction_FeatureImportance_.ipynb
@@ -0,0 +1,2756 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "id": "SbtDquK72fBs"
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "executionInfo": {
+ "elapsed": 724,
+ "status": "ok",
+ "timestamp": 1716047327679,
+ "user": {
+ "displayName": "Disha Mukherjee",
+ "userId": "03755156500668301044"
+ },
+ "user_tz": -330
+ },
+ "id": "bNpvJxBC3n1Y",
+ "outputId": "46c5ffc4-7875-4fa7-f1d2-f7839bb0062b"
+ },
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "for dirname, _, filenames in os.walk('/content/drive/MyDrive/Maternal Health Risk Prediction'):\n",
+ " for filename in filenames:\n",
+ " print(os.path.join(dirname, filename))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 206
+ },
+ "executionInfo": {
+ "elapsed": 454,
+ "status": "ok",
+ "timestamp": 1716047331878,
+ "user": {
+ "displayName": "Disha Mukherjee",
+ "userId": "03755156500668301044"
+ },
+ "user_tz": -330
+ },
+ "id": "9kDoM3nP4c_E",
+ "outputId": "c8093798-acda-486d-9190-1852d1877357"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Age | \n",
+ " SystolicBP | \n",
+ " DiastolicBP | \n",
+ " BS | \n",
+ " BodyTemp | \n",
+ " HeartRate | \n",
+ " RiskLevel | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " 25 | \n",
+ " 130 | \n",
+ " 80 | \n",
+ " 15.0 | \n",
+ " 98.0 | \n",
+ " 86 | \n",
+ " high risk | \n",
+ "
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+ " \n",
+ " 1 | \n",
+ " 35 | \n",
+ " 140 | \n",
+ " 90 | \n",
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+ " high risk | \n",
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+ " \n",
+ " 2 | \n",
+ " 29 | \n",
+ " 90 | \n",
+ " 70 | \n",
+ " 8.0 | \n",
+ " 100.0 | \n",
+ " 80 | \n",
+ " high risk | \n",
+ "
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+ " \n",
+ " 3 | \n",
+ " 30 | \n",
+ " 140 | \n",
+ " 85 | \n",
+ " 7.0 | \n",
+ " 98.0 | \n",
+ " 70 | \n",
+ " high risk | \n",
+ "
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+ " \n",
+ " 4 | \n",
+ " 35 | \n",
+ " 120 | \n",
+ " 60 | \n",
+ " 6.1 | \n",
+ " 98.0 | \n",
+ " 76 | \n",
+ " low risk | \n",
+ "
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+ " \n",
+ "
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+ "
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+ ],
+ "text/plain": [
+ " Age SystolicBP DiastolicBP BS BodyTemp HeartRate RiskLevel\n",
+ "0 25 130 80 15.0 98.0 86 high risk\n",
+ "1 35 140 90 13.0 98.0 70 high risk\n",
+ "2 29 90 70 8.0 100.0 80 high risk\n",
+ "3 30 140 85 7.0 98.0 70 high risk\n",
+ "4 35 120 60 6.1 98.0 76 low risk"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data = pd.read_csv('Maternal Health Risk Data Set.csv')\n",
+ "data.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6NNR8U5t4rm2"
+ },
+ "source": [
+ "1.Variable Description and Identification"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "executionInfo": {
+ "elapsed": 439,
+ "status": "ok",
+ "timestamp": 1716047335961,
+ "user": {
+ "displayName": "Disha Mukherjee",
+ "userId": "03755156500668301044"
+ },
+ "user_tz": -330
+ },
+ "id": "mc5ms_Rq4oce",
+ "outputId": "d785d2c7-5906-49ad-db3e-ef700ed2ab8d"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "RangeIndex: 1014 entries, 0 to 1013\n",
+ "Data columns (total 7 columns):\n",
+ " # Column Non-Null Count Dtype \n",
+ "--- ------ -------------- ----- \n",
+ " 0 Age 1014 non-null int64 \n",
+ " 1 SystolicBP 1014 non-null int64 \n",
+ " 2 DiastolicBP 1014 non-null int64 \n",
+ " 3 BS 1014 non-null float64\n",
+ " 4 BodyTemp 1014 non-null float64\n",
+ " 5 HeartRate 1014 non-null int64 \n",
+ " 6 RiskLevel 1014 non-null object \n",
+ "dtypes: float64(2), int64(4), object(1)\n",
+ "memory usage: 55.6+ KB\n"
+ ]
+ }
+ ],
+ "source": [
+ "data.info()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "LrODy_wA5Op5"
+ },
+ "source": [
+ "1.1 Checking missing/null values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "executionInfo": {
+ "elapsed": 3,
+ "status": "ok",
+ "timestamp": 1716047339031,
+ "user": {
+ "displayName": "Disha Mukherjee",
+ "userId": "03755156500668301044"
+ },
+ "user_tz": -330
+ },
+ "id": "xLD7JIBc5KQ9",
+ "outputId": "66269f04-4ea9-42ef-be50-8dc4f1f5a642"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Age 0\n",
+ "SystolicBP 0\n",
+ "DiastolicBP 0\n",
+ "BS 0\n",
+ "BodyTemp 0\n",
+ "HeartRate 0\n",
+ "RiskLevel 0\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data.isnull().sum()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "VBx2zQP_8W0r"
+ },
+ "source": [
+ "1.2 Checking duplicates"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 441
+ },
+ "executionInfo": {
+ "elapsed": 461,
+ "status": "ok",
+ "timestamp": 1716047342207,
+ "user": {
+ "displayName": "Disha Mukherjee",
+ "userId": "03755156500668301044"
+ },
+ "user_tz": -330
+ },
+ "id": "b-9U63yU8VLg",
+ "outputId": "ef04c717-a29a-47cf-c3fd-c1c86e0d8efa"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "There are 562 duplicates data\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
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+ "\n",
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+ " \n",
+ " \n",
+ " | \n",
+ " Age | \n",
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+ " DiastolicBP | \n",
+ " BS | \n",
+ " BodyTemp | \n",
+ " HeartRate | \n",
+ " RiskLevel | \n",
+ "
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+ " \n",
+ " \n",
+ " \n",
+ " 670 | \n",
+ " 10 | \n",
+ " 100 | \n",
+ " 50 | \n",
+ " 6.0 | \n",
+ " 99.0 | \n",
+ " 70 | \n",
+ " mid risk | \n",
+ "
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+ " \n",
+ " 849 | \n",
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+ " 6.0 | \n",
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+ "
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+ " \n",
+ " 552 | \n",
+ " 12 | \n",
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+ " 7.5 | \n",
+ " 102.0 | \n",
+ " 60 | \n",
+ " low risk | \n",
+ "
\n",
+ " \n",
+ " 940 | \n",
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+ " 60 | \n",
+ " 7.5 | \n",
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+ " 60 | \n",
+ " low risk | \n",
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+ " \n",
+ " 543 | \n",
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+ " \n",
+ " ... | \n",
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+ "
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+ " 772 | \n",
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+ " \n",
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+ " \n",
+ "
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+ "
866 rows × 7 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Age SystolicBP DiastolicBP BS BodyTemp HeartRate RiskLevel\n",
+ "670 10 100 50 6.0 99.0 70 mid risk\n",
+ "849 10 100 50 6.0 99.0 70 mid risk\n",
+ "552 12 90 60 7.5 102.0 60 low risk\n",
+ "940 12 90 60 7.5 102.0 60 low risk\n",
+ "543 12 90 60 7.5 102.0 66 low risk\n",
+ ".. ... ... ... ... ... ... ...\n",
+ "553 60 120 85 15.0 98.0 60 mid risk\n",
+ "772 60 120 85 15.0 98.0 60 mid risk\n",
+ "818 60 120 85 15.0 98.0 60 mid risk\n",
+ "114 63 140 90 15.0 98.0 90 high risk\n",
+ "502 63 140 90 15.0 98.0 90 high risk\n",
+ "\n",
+ "[866 rows x 7 columns]"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "print(f\"There are {data.duplicated().sum()} duplicates data\")\n",
+ "data.loc[data.duplicated(keep=False)].sort_values(by=data.columns.to_list())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "C-H0Y7qu8h7g"
+ },
+ "source": [
+ "2. Univariate Analysis"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "HLorMUjJ8m3f"
+ },
+ "source": [
+ "2.1 Categorical Variables"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "QI6c2KzY8tE9"
+ },
+ "source": [
+ "2.1.1 Risk Level"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 497
+ },
+ "executionInfo": {
+ "elapsed": 1391,
+ "status": "ok",
+ "timestamp": 1716047352051,
+ "user": {
+ "displayName": "Disha Mukherjee",
+ "userId": "03755156500668301044"
+ },
+ "user_tz": -330
+ },
+ "id": "PkGmcH-C8djE",
+ "outputId": "17852d21-e591-493a-8902-ea94c948f8d6"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\divya\\AppData\\Local\\Temp\\ipykernel_28628\\2935681441.py:9: FutureWarning: \n",
+ "\n",
+ "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+ "\n",
+ " count = sns.countplot(x=\"RiskLevel\", data=data, ax=ax[1], order=risk_order, palette=p_colors)\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "p_colors = ['green', 'orange', 'red']\n",
+ "risk_order = [\"low risk\", \"mid risk\", \"high risk\"]\n",
+ "fig, ax = plt.subplots(1, 2, figsize=(12, 4))\n",
+ "\n",
+ "data[\"RiskLevel\"].value_counts().plot(kind=\"pie\", labels=risk_order, colors=p_colors, explode=[0.05, 0.05, 0.05], autopct='%1.1f%%', ax=ax[0], shadow=True)\n",
+ "ax[0].set_title(\"Risk Level Pie Chart\")\n",
+ "ax[0].set_ylabel('')\n",
+ "\n",
+ "count = sns.countplot(x=\"RiskLevel\", data=data, ax=ax[1], order=risk_order, palette=p_colors)\n",
+ "for bar in count.patches:\n",
+ " count.annotate(format(bar.get_height()),\n",
+ " (bar.get_x() + bar.get_width() / 2,\n",
+ " bar.get_height()), ha='center', va='center',\n",
+ " size=11, xytext=(0, 8),\n",
+ " textcoords='offset points')\n",
+ "ax[1].set_title(\"Risk Level Bar Chart\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "C-B_OTYRWzQ1"
+ },
+ "source": [
+ "The majority of expectant women in this dataset appear to be at low risk for health issues. Pregnant women in 406 (40%) of the 1014 observations have low risk, 336 (33.1%) have medium risk, and 272 (26.8%) have high risk. To learn more and understand why pregnant women are at a different health risk, we will examine the data. Every variable that could have an impact on it will be examined."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Rk-MWWnq84JV"
+ },
+ "source": [
+ "2.2 Numerical Variables"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 300
+ },
+ "executionInfo": {
+ "elapsed": 436,
+ "status": "ok",
+ "timestamp": 1716047356023,
+ "user": {
+ "displayName": "Disha Mukherjee",
+ "userId": "03755156500668301044"
+ },
+ "user_tz": -330
+ },
+ "id": "yWXIFPVw80o1",
+ "outputId": "c3c00197-654b-4cc3-f117-57cea793444a"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Age | \n",
+ " SystolicBP | \n",
+ " DiastolicBP | \n",
+ " BS | \n",
+ " BodyTemp | \n",
+ " HeartRate | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " count | \n",
+ " 1014.000000 | \n",
+ " 1014.000000 | \n",
+ " 1014.000000 | \n",
+ " 1014.000000 | \n",
+ " 1014.000000 | \n",
+ " 1014.000000 | \n",
+ "
\n",
+ " \n",
+ " mean | \n",
+ " 29.871795 | \n",
+ " 113.198225 | \n",
+ " 76.460552 | \n",
+ " 8.725986 | \n",
+ " 98.665089 | \n",
+ " 74.301775 | \n",
+ "
\n",
+ " \n",
+ " std | \n",
+ " 13.474386 | \n",
+ " 18.403913 | \n",
+ " 13.885796 | \n",
+ " 3.293532 | \n",
+ " 1.371384 | \n",
+ " 8.088702 | \n",
+ "
\n",
+ " \n",
+ " min | \n",
+ " 10.000000 | \n",
+ " 70.000000 | \n",
+ " 49.000000 | \n",
+ " 6.000000 | \n",
+ " 98.000000 | \n",
+ " 7.000000 | \n",
+ "
\n",
+ " \n",
+ " 25% | \n",
+ " 19.000000 | \n",
+ " 100.000000 | \n",
+ " 65.000000 | \n",
+ " 6.900000 | \n",
+ " 98.000000 | \n",
+ " 70.000000 | \n",
+ "
\n",
+ " \n",
+ " 50% | \n",
+ " 26.000000 | \n",
+ " 120.000000 | \n",
+ " 80.000000 | \n",
+ " 7.500000 | \n",
+ " 98.000000 | \n",
+ " 76.000000 | \n",
+ "
\n",
+ " \n",
+ " 75% | \n",
+ " 39.000000 | \n",
+ " 120.000000 | \n",
+ " 90.000000 | \n",
+ " 8.000000 | \n",
+ " 98.000000 | \n",
+ " 80.000000 | \n",
+ "
\n",
+ " \n",
+ " max | \n",
+ " 70.000000 | \n",
+ " 160.000000 | \n",
+ " 100.000000 | \n",
+ " 19.000000 | \n",
+ " 103.000000 | \n",
+ " 90.000000 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Age SystolicBP DiastolicBP BS BodyTemp \\\n",
+ "count 1014.000000 1014.000000 1014.000000 1014.000000 1014.000000 \n",
+ "mean 29.871795 113.198225 76.460552 8.725986 98.665089 \n",
+ "std 13.474386 18.403913 13.885796 3.293532 1.371384 \n",
+ "min 10.000000 70.000000 49.000000 6.000000 98.000000 \n",
+ "25% 19.000000 100.000000 65.000000 6.900000 98.000000 \n",
+ "50% 26.000000 120.000000 80.000000 7.500000 98.000000 \n",
+ "75% 39.000000 120.000000 90.000000 8.000000 98.000000 \n",
+ "max 70.000000 160.000000 100.000000 19.000000 103.000000 \n",
+ "\n",
+ " HeartRate \n",
+ "count 1014.000000 \n",
+ "mean 74.301775 \n",
+ "std 8.088702 \n",
+ "min 7.000000 \n",
+ "25% 70.000000 \n",
+ "50% 76.000000 \n",
+ "75% 80.000000 \n",
+ "max 90.000000 "
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data.describe()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "NYHTUxu9W9yg"
+ },
+ "source": [
+ "As we can see, it appears that there is an outlier in the Age, BS, and HeartRate variables. However, this is only an educated guess."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "id": "SKQM6ID39Ckp"
+ },
+ "outputs": [],
+ "source": [
+ "def num_plot(data, col):\n",
+ " fig, ax = plt.subplots(1, 2, figsize=(12, 4))\n",
+ "\n",
+ " sns.histplot(data=data, x=col, kde=True, ax=ax[0])\n",
+ " sns.boxplot(data=data, x=col, ax=ax[1])\n",
+ " ax[0].set_title(f\"{col} Distribution Histogram\")\n",
+ " ax[1].set_title(f\"{col} Distribution Boxplot\")\n",
+ "\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "dG8vOOf69Fs2"
+ },
+ "source": [
+ "2.2.1 Age"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 410
+ },
+ "executionInfo": {
+ "elapsed": 1384,
+ "status": "ok",
+ "timestamp": 1716047360084,
+ "user": {
+ "displayName": "Disha Mukherjee",
+ "userId": "03755156500668301044"
+ },
+ "user_tz": -330
+ },
+ "id": "sa4jRh789IFw",
+ "outputId": "28034a48-18e6-4d65-ed4e-d2e5b210cb2f"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
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