diff --git a/Maternal Health Risk Prediction/Maternal Health Risk Data Set.csv b/Maternal Health Risk Prediction/Maternal Health Risk Data Set.csv
new file mode 100644
index 00000000..d58d799f
--- /dev/null
+++ b/Maternal Health Risk Prediction/Maternal Health Risk Data Set.csv	
@@ -0,0 +1,1015 @@
+Age,SystolicBP,DiastolicBP,BS,BodyTemp,HeartRate,RiskLevel
+25,130,80,15,98,86,high risk
+35,140,90,13,98,70,high risk
+29,90,70,8,100,80,high risk
+30,140,85,7,98,70,high risk
+35,120,60,6.1,98,76,low risk
+23,140,80,7.01,98,70,high risk
+23,130,70,7.01,98,78,mid risk
+35,85,60,11,102,86,high risk
+32,120,90,6.9,98,70,mid risk
+42,130,80,18,98,70,high risk
+23,90,60,7.01,98,76,low risk
+19,120,80,7,98,70,mid risk
+25,110,89,7.01,98,77,low risk
+20,120,75,7.01,100,70,mid risk
+48,120,80,11,98,88,mid risk
+15,120,80,7.01,98,70,low risk
+50,140,90,15,98,90,high risk
+25,140,100,7.01,98,80,high risk
+30,120,80,6.9,101,76,mid risk
+10,70,50,6.9,98,70,low risk
+40,140,100,18,98,90,high risk
+50,140,80,6.7,98,70,mid risk
+21,90,65,7.5,98,76,low risk
+18,90,60,7.5,98,70,low risk
+21,120,80,7.5,98,76,low risk
+16,100,70,7.2,98,80,low risk
+19,120,75,7.2,98,66,low risk
+22,100,65,7.2,98,70,low risk
+49,120,90,7.2,98,77,low risk
+28,90,60,7.2,98,82,low risk
+20,100,90,7.1,98,88,low risk
+23,100,85,7.1,98,66,low risk
+22,120,90,7.1,98,82,low risk
+21,120,80,7.1,98,77,low risk
+21,75,50,6.1,98,70,low risk
+12,95,60,6.1,102,60,low risk
+60,120,80,6.1,98,75,low risk
+55,100,65,6.1,98,66,low risk
+45,120,95,6.1,98,66,low risk
+35,100,70,6.1,98,66,low risk
+22,120,85,6.1,98,88,low risk
+23,120,90,6.1,98,60,low risk
+25,90,70,6.1,98,80,low risk
+30,120,80,6.1,98,70,low risk
+23,120,90,6.1,98,70,low risk
+32,120,90,7.5,98,70,low risk
+42,120,80,7.5,98,70,low risk
+23,90,60,7.5,98,76,low risk
+15,76,49,7.5,98,77,low risk
+15,120,80,7,98,70,low risk
+25,120,80,7,98,66,low risk
+22,100,65,7,98,80,low risk
+35,100,70,7,98,60,low risk
+19,120,85,7,98,60,low risk
+60,90,65,7,98,77,low risk
+23,120,90,6.7,98,70,low risk
+32,120,90,6.4,98,70,low risk
+42,120,80,6.4,98,70,low risk
+23,90,60,6.4,98,76,low risk
+15,76,49,6.4,98,77,low risk
+15,120,80,7.2,98,70,low risk
+15,80,60,7,98,80,low risk
+12,95,60,7.2,98,77,low risk
+29,90,70,6.7,98,80,mid risk
+31,120,60,6.1,98,76,mid risk
+29,130,70,6.7,98,78,mid risk
+17,85,60,9,102,86,mid risk
+19,120,80,7,98,70,mid risk
+20,110,60,7,100,70,mid risk
+32,120,65,6,101,76,mid risk
+26,85,60,6,101,86,mid risk
+29,130,70,7.7,98,78,mid risk
+19,120,80,7,98,70,mid risk
+54,130,70,12,98,67,mid risk
+44,120,90,16,98,80,mid risk
+23,130,70,6.9,98,70,mid risk
+22,85,60,6.9,98,76,mid risk
+55,120,90,12,98,70,mid risk
+35,120,80,6.9,98,78,mid risk
+21,90,60,6.9,98,86,mid risk
+16,90,65,6.9,98,76,mid risk
+33,115,65,7,98,70,mid risk
+12,95,60,6.9,98,65,mid risk
+28,120,90,6.9,98,70,mid risk
+21,90,65,6.9,98,76,mid risk
+18,90,60,6.9,98,70,mid risk
+21,120,80,6.9,98,76,mid risk
+16,100,70,6.9,98,80,mid risk
+19,120,75,6.9,98,66,mid risk
+23,100,85,6.9,98,66,mid risk
+22,120,90,7.8,98,82,mid risk
+60,120,85,15,98,60,mid risk
+13,90,65,7.8,101,80,mid risk
+23,120,90,7.8,98,60,mid risk
+28,115,60,7.8,101,86,mid risk
+50,120,80,7.8,98,70,mid risk
+29,130,70,7.8,98,78,mid risk
+19,120,80,7,98,70,mid risk
+19,120,85,7.8,98,60,mid risk
+60,90,65,6.8,98,77,mid risk
+55,120,90,6.8,98,66,mid risk
+25,120,80,6.8,98,66,mid risk
+48,140,90,15,98,90,high risk
+25,140,100,6.8,98,80,high risk
+23,140,90,6.8,98,70,high risk
+34,85,60,11,102,86,high risk
+50,140,90,15,98,90,high risk
+25,140,100,6.8,98,80,high risk
+42,140,100,18,98,90,high risk
+32,140,100,7.9,98,78,high risk
+50,140,95,17,98,60,high risk
+38,135,60,7.9,101,86,high risk
+39,90,70,9,98,80,high risk
+30,140,100,15,98,70,high risk
+63,140,90,15,98,90,high risk
+25,140,100,7.9,98,80,high risk
+30,120,80,7.9,101,76,high risk
+55,140,100,18,98,90,high risk
+32,140,100,7.9,98,78,high risk
+30,140,100,15,98,70,high risk
+48,120,80,11,98,88,high risk
+49,140,90,15,98,90,high risk
+25,140,100,7.5,98,80,high risk
+40,160,100,19,98,77,high risk
+32,140,90,18,98,88,high risk
+35,140,100,7.5,98,66,high risk
+54,140,100,15,98,66,high risk
+55,140,95,19,98,77,high risk
+29,120,70,9,98,80,high risk
+48,120,80,11,98,88,high risk
+40,160,100,19,98,77,high risk
+32,140,90,18,98,88,high risk
+35,140,100,7.5,98,66,high risk
+54,140,100,15,98,66,high risk
+40,120,95,11,98,80,high risk
+22,90,60,7.5,102,60,high risk
+40,120,85,15,98,60,high risk
+55,140,95,19,98,77,high risk
+50,130,100,16,98,75,high risk
+18,120,80,6.9,102,76,mid risk
+32,140,100,6.9,98,78,high risk
+17,90,60,6.9,101,76,mid risk
+17,90,63,6.9,101,70,mid risk
+25,120,90,6.7,101,80,mid risk
+17,120,80,6.7,102,76,mid risk
+14,90,65,7,101,70,high risk
+15,80,60,6.7,98,80,low risk
+15,100,65,6.7,98,76,low risk
+12,95,60,6.7,98,77,low risk
+37,120,90,11,98,88,high risk
+18,100,70,6.7,98,76,low risk
+21,100,85,6.7,98,70,low risk
+17,110,75,12,101,76,high risk
+25,120,90,7.5,98,80,low risk
+23,85,65,7.5,98,70,low risk
+12,95,60,7.5,98,65,low risk
+28,120,90,7.5,98,70,low risk
+40,120,90,12,98,80,high risk
+55,129,85,7.5,98,88,low risk
+25,100,90,7.5,98,76,low risk
+35,120,80,7.5,98,80,low risk
+21,90,65,7.5,98,76,low risk
+18,90,60,7.5,98,70,low risk
+21,120,80,7.5,98,76,low risk
+16,100,70,7.2,98,80,low risk
+19,120,75,7.2,98,66,low risk
+40,160,100,19,98,77,high risk
+32,140,90,18,98,88,high risk
+22,100,65,7.2,98,70,low risk
+49,120,90,7.2,98,77,low risk
+28,90,60,7.2,98,82,low risk
+12,90,60,7.9,102,66,high risk
+20,100,90,7.1,98,88,low risk
+23,100,85,7.1,98,66,low risk
+22,120,90,7.1,98,82,low risk
+21,120,80,7.1,98,77,low risk
+35,140,100,8,98,66,high risk
+54,140,100,15,98,66,high risk
+40,120,95,11,98,80,high risk
+21,75,50,6.1,98,70,low risk
+12,95,60,6.1,102,60,low risk
+60,120,85,15,98,60,high risk
+55,140,95,19,98,77,high risk
+50,130,100,16,98,75,high risk
+60,120,80,6.1,98,75,low risk
+55,100,65,6.1,98,66,low risk
+45,120,95,6.1,98,66,low risk
+35,100,70,6.1,98,66,low risk
+22,120,85,6.1,98,88,low risk
+13,90,65,7.9,101,80,mid risk
+23,120,90,6.1,98,60,low risk
+17,90,65,6.1,103,67,high risk
+28,83,60,8,101,86,high risk
+50,120,80,15,98,70,high risk
+25,90,70,6.1,98,80,low risk
+30,120,80,6.1,98,70,low risk
+31,120,60,6.1,98,76,mid risk
+23,120,90,6.1,98,70,low risk
+29,130,70,6.1,98,78,mid risk
+17,85,60,9,102,86,high risk
+32,120,90,7.5,98,70,low risk
+42,120,80,7.5,98,70,low risk
+23,90,60,7.5,98,76,low risk
+19,120,80,7,98,70,mid risk
+15,76,49,7.5,98,77,low risk
+33,120,75,10,98,70,high risk
+48,120,80,11,98,88,high risk
+15,120,80,7,98,70,low risk
+25,120,80,7,98,66,low risk
+22,100,65,7,98,80,low risk
+50,140,95,17,98,60,high risk
+35,100,70,7,98,60,low risk
+19,120,85,7,98,60,low risk
+60,90,65,7,98,77,low risk
+28,85,60,9,101,86,mid risk
+50,140,80,6.7,98,70,mid risk
+29,90,70,6.7,98,80,mid risk
+30,140,100,15,98,70,high risk
+31,120,60,6.1,98,76,mid risk
+23,120,90,6.7,98,70,low risk
+29,130,70,6.7,98,78,mid risk
+17,85,60,9,102,86,mid risk
+32,120,90,6.4,98,70,low risk
+42,120,80,6.4,98,70,low risk
+23,90,60,6.4,98,76,low risk
+19,120,80,7,98,70,mid risk
+15,76,49,6.4,98,77,low risk
+29,120,75,7.2,100,70,high risk
+48,120,80,11,98,88,high risk
+15,120,80,7.2,98,70,low risk
+50,140,90,15,98,77,high risk
+25,140,100,7.2,98,80,high risk
+55,140,80,7.2,101,76,high risk
+20,110,60,7,100,70,mid risk
+40,140,100,18,98,77,high risk
+28,120,80,9,102,76,high risk
+32,140,100,8,98,70,high risk
+17,90,60,11,101,78,high risk
+17,90,63,8,101,70,high risk
+25,120,90,12,101,80,high risk
+17,120,80,7,102,76,high risk
+19,90,65,11,101,70,high risk
+15,80,60,7,98,80,low risk
+32,120,65,6,101,76,mid risk
+12,95,60,7.2,98,77,low risk
+37,120,90,11,98,88,high risk
+18,100,70,6.8,98,76,low risk
+21,100,85,6.9,98,70,low risk
+17,110,75,13,101,76,high risk
+25,120,90,15,98,80,high risk
+10,85,65,6.9,98,70,low risk
+12,95,60,6.9,98,65,low risk
+28,120,90,6.9,98,70,low risk
+40,120,90,6.9,98,80,low risk
+55,110,85,6.9,98,88,low risk
+25,100,90,6.9,98,76,low risk
+35,120,80,6.9,98,80,low risk
+21,90,65,6.9,98,76,low risk
+18,90,60,6.9,98,70,low risk
+21,120,80,6.9,98,76,low risk
+16,100,70,6.9,98,80,low risk
+19,120,75,6.9,98,66,low risk
+40,160,100,19,98,77,high risk
+32,140,90,18,98,88,high risk
+22,100,65,6.9,98,70,low risk
+49,120,90,6.9,98,77,low risk
+28,90,60,6.9,98,82,low risk
+12,90,60,8,102,66,high risk
+20,100,90,7,98,88,low risk
+23,100,85,7,98,66,low risk
+22,120,90,7,98,82,low risk
+21,120,80,7,98,77,low risk
+35,140,100,9,98,66,high risk
+54,140,100,15,98,66,high risk
+40,120,95,11,98,80,high risk
+21,75,50,7.7,98,60,low risk
+12,90,60,11,102,60,high risk
+60,120,85,15,98,60,high risk
+55,140,95,19,98,77,high risk
+50,130,100,16,98,76,high risk
+60,120,80,7.7,98,75,low risk
+55,100,65,7.7,98,66,low risk
+45,120,95,7.7,98,66,low risk
+35,100,70,7.7,98,66,low risk
+22,120,85,7.7,98,88,low risk
+13,90,65,9,101,80,high risk
+23,120,90,7.7,98,60,low risk
+17,90,65,7.7,103,67,high risk
+26,85,60,6,101,86,mid risk
+50,120,80,7.7,98,70,low risk
+19,90,70,7.7,98,80,low risk
+30,120,80,7.7,98,70,low risk
+31,120,60,6.1,98,76,low risk
+23,120,80,7.7,98,70,low risk
+29,130,70,7.7,98,78,mid risk
+17,85,60,6.3,102,86,high risk
+32,120,90,7.7,98,70,low risk
+42,120,80,7.7,98,70,low risk
+23,90,60,7.7,98,76,low risk
+19,120,80,7,98,70,mid risk
+15,75,49,7.7,98,77,low risk
+40,120,75,7.7,98,70,high risk
+48,120,80,11,98,88,high risk
+15,120,80,7.7,98,70,low risk
+25,120,80,7.7,98,66,low risk
+22,100,65,6.9,98,80,low risk
+12,120,95,6.9,98,60,low risk
+35,100,70,6.9,98,60,low risk
+19,120,85,6.9,98,60,low risk
+60,90,65,6.9,98,77,low risk
+55,120,90,6.9,98,76,low risk
+35,90,65,6.9,98,75,low risk
+51,85,60,6.9,98,66,low risk
+62,120,80,6.9,98,66,low risk
+25,90,70,6.9,98,66,low risk
+21,120,80,6.9,98,88,low risk
+22,120,60,15,98,80,high risk
+55,120,90,18,98,60,high risk
+54,130,70,12,98,67,mid risk
+35,85,60,19,98,86,high risk
+43,120,90,18,98,70,high risk
+12,120,80,6.9,98,80,low risk
+65,90,60,6.9,98,70,low risk
+60,120,80,6.9,98,76,low risk
+25,120,90,6.9,98,70,low risk
+22,90,65,6.9,98,78,low risk
+66,85,60,6.9,98,86,low risk
+56,120,80,13,98,70,high risk
+35,90,70,6.9,98,70,low risk
+43,120,80,15,98,76,high risk
+35,120,60,6.9,98,70,low risk
+44,120,90,16,98,80,mid risk
+23,130,70,6.9,98,70,mid risk
+22,85,60,6.9,98,76,mid risk
+55,120,90,12,98,70,mid risk
+35,120,80,6.9,98,78,mid risk
+21,90,60,6.9,98,86,mid risk
+45,120,80,6.9,103,70,low risk
+70,85,60,6.9,102,70,low risk
+65,120,90,6.9,103,76,low risk
+55,120,80,6.9,102,80,low risk
+45,90,60,18,101,70,high risk
+22,120,80,6.9,103,76,low risk
+16,90,65,6.9,98,76,mid risk
+12,95,60,6.9,98,77,low risk
+37,120,90,11,98,88,high risk
+18,100,70,6.9,98,76,low risk
+21,100,85,6.9,98,70,low risk
+17,110,75,6.9,101,76,high risk
+25,120,90,6.9,98,80,low risk
+33,115,65,7,98,70,mid risk
+12,95,60,6.9,98,65,mid risk
+28,120,90,6.9,98,70,mid risk
+40,120,90,6.9,98,80,high risk
+55,110,85,6.9,98,88,high risk
+25,100,90,6.9,98,76,high risk
+35,120,80,6.9,98,80,high risk
+21,90,65,6.9,98,76,mid risk
+18,90,60,6.9,98,70,mid risk
+21,120,80,6.9,98,76,mid risk
+16,100,70,6.9,98,80,mid risk
+19,120,75,6.9,98,66,mid risk
+40,160,100,19,98,77,high risk
+32,140,90,18,98,88,high risk
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+22,120,90,7.8,98,82,mid risk
+21,120,80,7.8,98,77,low risk
+35,140,100,7.8,98,66,high risk
+54,140,100,15,98,66,high risk
+40,120,95,11,98,80,high risk
+21,75,50,7.8,98,60,low risk
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+50,130,100,16,98,75,high risk
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+55,100,65,7.8,98,66,low risk
+45,120,95,7.8,98,66,low risk
+35,100,70,7.8,98,66,low risk
+22,120,85,7.8,98,88,low risk
+13,90,65,7.8,101,80,mid risk
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+17,90,65,7.8,103,67,high risk
+28,115,60,7.8,101,86,mid risk
+50,120,80,7.8,98,70,mid risk
+19,90,70,7.8,98,80,low risk
+30,120,80,7.8,98,70,low risk
+31,120,60,6.1,98,76,low risk
+23,120,70,7.8,98,70,low risk
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+42,120,80,7.8,98,70,low risk
+23,90,60,7.8,98,76,low risk
+19,120,80,7,98,70,mid risk
+15,76,49,7.8,98,77,low risk
+20,120,75,7.8,98,70,low risk
+48,120,80,11,98,88,high risk
+15,120,80,7.8,98,70,low risk
+25,120,80,7.8,98,66,low risk
+22,100,65,7.8,98,80,low risk
+12,120,95,7.8,98,60,low risk
+35,100,70,7.8,98,60,low risk
+19,120,85,7.8,98,60,mid risk
+60,90,65,6.8,98,77,mid risk
+55,120,90,6.8,98,66,mid risk
+25,120,80,6.8,98,66,mid risk
+22,100,65,6.8,98,88,low risk
+12,120,95,6.8,98,60,mid risk
+35,100,70,6.8,98,60,mid risk
+19,120,90,6.8,98,60,mid risk
+60,90,65,6.8,98,77,mid risk
+55,120,90,6.8,98,78,low risk
+50,130,80,16,102,76,mid risk
+27,120,90,6.8,102,68,mid risk
+60,140,90,12,98,77,high risk
+55,100,70,6.8,101,80,mid risk
+60,140,80,16,98,66,high risk
+12,120,90,6.8,98,80,mid risk
+17,140,100,6.8,103,80,high risk
+60,120,80,6.8,98,77,mid risk
+22,100,65,6.8,98,88,low risk
+36,140,100,6.8,102,76,high risk
+22,90,60,6.8,98,77,low risk
+25,120,100,6.8,98,60,mid risk
+35,100,60,15,98,80,high risk
+40,140,100,13,101,66,high risk
+27,120,70,6.8,98,77,low risk
+36,140,100,6.8,102,76,high risk
+22,90,60,6.8,98,77,mid risk
+25,120,100,6.8,98,60,low risk
+35,100,60,15,98,80,high risk
+40,140,100,13,101,66,high risk
+27,120,70,6.8,98,77,low risk
+27,120,70,6.8,98,77,low risk
+65,130,80,15,98,86,high risk
+35,140,80,13,98,70,high risk
+29,90,70,10,98,80,high risk
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diff --git a/Maternal Health Risk Prediction/Maternal_health_risk_prediction_dataset_exploration.ipynb b/Maternal Health Risk Prediction/Maternal_health_risk_prediction_dataset_exploration.ipynb
new file mode 100644
index 00000000..6b11828f
--- /dev/null
+++ b/Maternal Health Risk Prediction/Maternal_health_risk_prediction_dataset_exploration.ipynb	
@@ -0,0 +1 @@
+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"mount_file_id":"1ipQRvFBk2ARqzri369uRjl4k2GNYf8ei","authorship_tag":"ABX9TyPHGg72PdfLEWqzNQectcv4"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":1,"metadata":{"id":"tjNIEopVQdD0","executionInfo":{"status":"ok","timestamp":1715525103055,"user_tz":-330,"elapsed":3118,"user":{"displayName":"Disha Mukherjee","userId":"03755156500668301044"}}},"outputs":[],"source":["#Import dependencies\n","import pandas as pd\n","import seaborn as sns\n","import numpy as np\n","from sklearn.model_selection import train_test_split\n","from sklearn.preprocessing import StandardScaler\n","from sklearn.metrics import confusion_matrix, accuracy_score\n","from sklearn.metrics import classification_report"]},{"cell_type":"code","source":["#Load the data from the CSV using The Data Frame\n","data = pd.read_csv(\"/content/drive/MyDrive/Maternal Health Risk Prediction(GSSOC'24)/Maternal Health Risk Data Set.csv\")"],"metadata":{"id":"bJnkR2FGRPvr","executionInfo":{"status":"ok","timestamp":1715525271633,"user_tz":-330,"elapsed":402,"user":{"displayName":"Disha Mukherjee","userId":"03755156500668301044"}}},"execution_count":2,"outputs":[]},{"cell_type":"code","source":["#Print the first five rows from this dataset\n","data.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"Iq7HQkcARkvg","executionInfo":{"status":"ok","timestamp":1715525288838,"user_tz":-330,"elapsed":434,"user":{"displayName":"Disha Mukherjee","userId":"03755156500668301044"}},"outputId":"992a0129-d3dd-4de1-df34-2c2711691006"},"execution_count":3,"outputs":[{"output_type":"execute_result","data":{"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"],"text/html":["\n","  <div id=\"df-88da92f9-cf37-4cc6-b018-2f240c6a1eda\" class=\"colab-df-container\">\n","    <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>Age</th>\n","      <th>SystolicBP</th>\n","      <th>DiastolicBP</th>\n","      <th>BS</th>\n","      <th>BodyTemp</th>\n","      <th>HeartRate</th>\n","      <th>RiskLevel</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n"," 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98.4,\n          98.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HeartRate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 7,\n        \"max\": 90,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          86,\n          70,\n          77\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RiskLevel\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"high risk\",\n          \"low risk\",\n          \"mid risk\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":3}]},{"cell_type":"code","source":["data.tail()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"4lyCm1CdRpQi","executionInfo":{"status":"ok","timestamp":1715525312137,"user_tz":-330,"elapsed":438,"user":{"displayName":"Disha Mukherjee","userId":"03755156500668301044"}},"outputId":"ae78ec71-f3d1-4fc5-eb34-ba40198f68ad"},"execution_count":4,"outputs":[{"output_type":"execute_result","data":{"text/plain":["      Age  SystolicBP  DiastolicBP    BS  BodyTemp  HeartRate  RiskLevel\n","1009   22         120           60  15.0      98.0         80  high risk\n","1010   55         120           90  18.0      98.0         60  high risk\n","1011   35          85           60  19.0      98.0         86  high risk\n","1012   43         120           90  18.0      98.0         70  high risk\n","1013   32         120           65   6.0     101.0         76   mid risk"],"text/html":["\n","  <div 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\"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15,\n        \"min\": 60,\n        \"max\": 90,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          60,\n          90\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.357238094391549,\n        \"min\": 6.0,\n        \"max\": 19.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          18.0,\n          6.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BodyTemp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.3416407864998738,\n        \"min\": 98.0,\n        \"max\": 101.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          101.0,\n          98.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HeartRate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9,\n        \"min\": 60,\n        \"max\": 86,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          60,\n          76\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RiskLevel\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"mid risk\",\n          \"high risk\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":4}]},{"cell_type":"code","source":["# To show any null or nan values in this data frame\n","data.isnull().sum()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3OQvEHECRuko","executionInfo":{"status":"ok","timestamp":1715525327807,"user_tz":-330,"elapsed":408,"user":{"displayName":"Disha Mukherjee","userId":"03755156500668301044"}},"outputId":"36fa6e43-ffbb-420d-f50f-aa2f5a9f4950"},"execution_count":5,"outputs":[{"output_type":"execute_result","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"]},"metadata":{},"execution_count":5}]},{"cell_type":"code","source":["#To show any duplicate value in this data Fram\n","data.duplicated().sum()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"g3xkV2jPSKj1","executionInfo":{"status":"ok","timestamp":1715525446894,"user_tz":-330,"elapsed":410,"user":{"displayName":"Disha Mukherjee","userId":"03755156500668301044"}},"outputId":"140797ea-9ee7-429a-d8c4-459e745589fc"},"execution_count":6,"outputs":[{"output_type":"execute_result","data":{"text/plain":["562"]},"metadata":{},"execution_count":6}]},{"cell_type":"code","source":["#Duplicated value\n","data.duplicated().any()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Nl-BQrjWSPK3","executionInfo":{"status":"ok","timestamp":1715525459797,"user_tz":-330,"elapsed":726,"user":{"displayName":"Disha Mukherjee","userId":"03755156500668301044"}},"outputId":"05abc2b3-4094-433b-9db1-7cbc0bc9db38"},"execution_count":7,"outputs":[{"output_type":"execute_result","data":{"text/plain":["True"]},"metadata":{},"execution_count":7}]},{"cell_type":"code","source":["#To drop duplicate values in this data frame\n","data.drop_duplicates(inplace=True)"],"metadata":{"id":"Gk4aHcjKSSLX","executionInfo":{"status":"ok","timestamp":1715525473993,"user_tz":-330,"elapsed":459,"user":{"displayName":"Disha Mukherjee","userId":"03755156500668301044"}}},"execution_count":8,"outputs":[]},{"cell_type":"code","source":["data.duplicated().any()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"35HJrw3vSVid","executionInfo":{"status":"ok","timestamp":1715525485477,"user_tz":-330,"elapsed":401,"user":{"displayName":"Disha Mukherjee","userId":"03755156500668301044"}},"outputId":"6bb40bd8-3135-4d54-d547-057ba7d4c8be"},"execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/plain":["False"]},"metadata":{},"execution_count":9}]},{"cell_type":"code","source":["data.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"EfD_ZWM2SaLd","executionInfo":{"status":"ok","timestamp":1715525498240,"user_tz":-330,"elapsed":401,"user":{"displayName":"Disha Mukherjee","userId":"03755156500668301044"}},"outputId":"5744838f-7ff5-45f1-fdae-1489abcdafda"},"execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(452, 7)"]},"metadata":{},"execution_count":10}]},{"cell_type":"code","source":["# Numbers of unique values\n","data[\"Age\"].unique()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"IYzBVuvQSbi1","executionInfo":{"status":"ok","timestamp":1715525512489,"user_tz":-330,"elapsed":396,"user":{"displayName":"Disha Mukherjee","userId":"03755156500668301044"}},"outputId":"ab4208ed-9578-4219-cc28-d57124b33848"},"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([25, 35, 29, 30, 23, 32, 42, 19, 20, 48, 15, 50, 10, 40, 21, 18, 16,\n","       22, 49, 28, 12, 60, 55, 45, 31, 17, 26, 54, 44, 33, 13, 34, 38, 39,\n","       63, 14, 37, 51, 62, 43, 65, 66, 56, 70, 27, 36, 59, 24, 41, 46])"]},"metadata":{},"execution_count":11}]},{"cell_type":"code","source":["data[\"SystolicBP\"].unique()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"G_37WdV5Se88","executionInfo":{"status":"ok","timestamp":1715525525217,"user_tz":-330,"elapsed":422,"user":{"displayName":"Disha Mukherjee","userId":"03755156500668301044"}},"outputId":"aa838139-ba52-44cf-9685-d6f9118f858e"},"execution_count":12,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([130, 140,  90, 120,  85, 110,  70, 100,  75,  95,  76,  80, 115,\n","       135, 160, 129,  83,  99,  78])"]},"metadata":{},"execution_count":12}]},{"cell_type":"code","source":["data[\"DiastolicBP\"].unique()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SM257slBSiKS","executionInfo":{"status":"ok","timestamp":1715525538052,"user_tz":-330,"elapsed":419,"user":{"displayName":"Disha Mukherjee","userId":"03755156500668301044"}},"outputId":"22993163-32e4-46f3-92d4-b1d2a4b7e5d0"},"execution_count":13,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([ 80,  90,  70,  85,  60,  89,  75, 100,  50,  65,  95,  49,  63,\n","        69,  76,  68])"]},"metadata":{},"execution_count":13}]},{"cell_type":"code","source":["data[\"BS\"].unique()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"l7RrDzmASlS_","executionInfo":{"status":"ok","timestamp":1715525549425,"user_tz":-330,"elapsed":438,"user":{"displayName":"Disha Mukherjee","userId":"03755156500668301044"}},"outputId":"2b44f059-b6de-4a3b-cec5-50669d583774"},"execution_count":14,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([15.  , 13.  ,  8.  ,  7.  ,  6.1 ,  7.01, 11.  ,  6.9 , 18.  ,\n","        6.7 ,  7.5 ,  7.2 ,  7.1 ,  6.4 ,  9.  ,  6.  ,  7.7 , 12.  ,\n","       16.  ,  7.8 ,  6.8 ,  7.9 , 17.  , 19.  , 10.  ,  6.3 ,  6.6 ,\n","        6.5 ,  7.6 ])"]},"metadata":{},"execution_count":14}]},{"cell_type":"code","source":["data[\"BodyTemp\"].unique()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HcnFgoc7Sx1C","executionInfo":{"status":"ok","timestamp":1715525604863,"user_tz":-330,"elapsed":435,"user":{"displayName":"Disha Mukherjee","userId":"03755156500668301044"}},"outputId":"6dd25509-02ad-40b6-cb2f-544ba665244a"},"execution_count":15,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([ 98. , 100. , 102. , 101. , 103. ,  98.4,  99. ,  98.6])"]},"metadata":{},"execution_count":15}]},{"cell_type":"code","source":["data[\"HeartRate\"].unique()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eUwIa9ldS1yw","executionInfo":{"status":"ok","timestamp":1715525616662,"user_tz":-330,"elapsed":532,"user":{"displayName":"Disha Mukherjee","userId":"03755156500668301044"}},"outputId":"1cc78a80-9f8f-4611-c0c2-d8b2ade11326"},"execution_count":16,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([86, 70, 80, 76, 78, 77, 88, 90, 66, 82, 60, 75, 67, 65, 68,  7])"]},"metadata":{},"execution_count":16}]},{"cell_type":"code","source":["data[\"RiskLevel\"].unique()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7q67ufOmS4YE","executionInfo":{"status":"ok","timestamp":1715525629849,"user_tz":-330,"elapsed":426,"user":{"displayName":"Disha Mukherjee","userId":"03755156500668301044"}},"outputId":"3dc32ac5-8426-47ba-e299-190826daee25"},"execution_count":17,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array(['high risk', 'low risk', 'mid risk'], dtype=object)"]},"metadata":{},"execution_count":17}]},{"cell_type":"code","source":["ca_val = [column for column in data.columns if data[column].nunique() <= 10]"],"metadata":{"id":"CcAUa5-ZS8gx","executionInfo":{"status":"ok","timestamp":1715525647403,"user_tz":-330,"elapsed":402,"user":{"displayName":"Disha Mukherjee","userId":"03755156500668301044"}}},"execution_count":18,"outputs":[]},{"cell_type":"code","source":["#Categorical Data\n","ca_val"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pBfIcHrpTLmf","executionInfo":{"status":"ok","timestamp":1715525707835,"user_tz":-330,"elapsed":806,"user":{"displayName":"Disha Mukherjee","userId":"03755156500668301044"}},"outputId":"b35382ba-60ec-469c-cb9a-946648c2f7a6"},"execution_count":19,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['BodyTemp', 'RiskLevel']"]},"metadata":{},"execution_count":19}]},{"cell_type":"code","source":["co_val = [column for column in data.columns if data[column].nunique() > 10]"],"metadata":{"id":"ljd7I9S6TOtQ","executionInfo":{"status":"ok","timestamp":1715525721359,"user_tz":-330,"elapsed":457,"user":{"displayName":"Disha Mukherjee","userId":"03755156500668301044"}}},"execution_count":20,"outputs":[]},{"cell_type":"code","source":["co_val"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Ulsb9E_nTSEQ","executionInfo":{"status":"ok","timestamp":1715525733800,"user_tz":-330,"elapsed":391,"user":{"displayName":"Disha Mukherjee","userId":"03755156500668301044"}},"outputId":"11b7f310-5840-45a5-ff04-6fd89fbe05bb"},"execution_count":21,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['Age', 'SystolicBP', 'DiastolicBP', 'BS', 'HeartRate']"]},"metadata":{},"execution_count":21}]},{"cell_type":"code","source":["data[\"BodyTemp\"].unique()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3B8O10IcTU-W","executionInfo":{"status":"ok","timestamp":1715525745251,"user_tz":-330,"elapsed":410,"user":{"displayName":"Disha Mukherjee","userId":"03755156500668301044"}},"outputId":"cb5bc55e-488f-4eeb-e599-f420a06c3374"},"execution_count":22,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([ 98. , 100. , 102. , 101. , 103. ,  98.4,  99. ,  98.6])"]},"metadata":{},"execution_count":22}]},{"cell_type":"code","source":["data[\"RiskLevel\"].unique()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vc-drablcgws","executionInfo":{"status":"ok","timestamp":1715528156414,"user_tz":-330,"elapsed":1368,"user":{"displayName":"Disha Mukherjee","userId":"03755156500668301044"}},"outputId":"16b9d451-9574-43be-9187-759f98ca14b8"},"execution_count":23,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array(['high risk', 'low risk', 'mid risk'], dtype=object)"]},"metadata":{},"execution_count":23}]},{"cell_type":"code","source":["co_val"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SF4oNmIMclFM","executionInfo":{"status":"ok","timestamp":1715528181105,"user_tz":-330,"elapsed":1355,"user":{"displayName":"Disha Mukherjee","userId":"03755156500668301044"}},"outputId":"60a9ef1d-07dd-433e-c027-edfb065bb8aa"},"execution_count":24,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['Age', 'SystolicBP', 'DiastolicBP', 'BS', 'HeartRate']"]},"metadata":{},"execution_count":24}]},{"cell_type":"code","source":["data[\"Age\"].unique()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Uzd0lmkTcqiw","executionInfo":{"status":"ok","timestamp":1715528193724,"user_tz":-330,"elapsed":664,"user":{"displayName":"Disha Mukherjee","userId":"03755156500668301044"}},"outputId":"2f823f3c-b480-41c8-dc50-fba53c24e7f0"},"execution_count":25,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([25, 35, 29, 30, 23, 32, 42, 19, 20, 48, 15, 50, 10, 40, 21, 18, 16,\n","       22, 49, 28, 12, 60, 55, 45, 31, 17, 26, 54, 44, 33, 13, 34, 38, 39,\n","       63, 14, 37, 51, 62, 43, 65, 66, 56, 70, 27, 36, 59, 24, 41, 46])"]},"metadata":{},"execution_count":25}]},{"cell_type":"code","source":["# Maping\n","risklevel = {\"low risk\" : 1,\n","            \"mid risk\" : 2,\n","            \"high risk\" : 3}\n","\n","# Apply Using Map\n","data[\"RiskLevel\"] = data[\"RiskLevel\"].map(risklevel).astype(float)\n","data"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":423},"id":"KcWYubFBctoA","executionInfo":{"status":"ok","timestamp":1715528208371,"user_tz":-330,"elapsed":434,"user":{"displayName":"Disha Mukherjee","userId":"03755156500668301044"}},"outputId":"d2bd52a0-bc9d-4a2d-f864-1aa406a49cf8"},"execution_count":26,"outputs":[{"output_type":"execute_result","data":{"text/plain":["     Age  SystolicBP  DiastolicBP    BS  BodyTemp  HeartRate  RiskLevel\n","0     25         130           80  15.0      98.0         86        3.0\n","1     35         140           90  13.0      98.0         70        3.0\n","2     29          90           70   8.0     100.0         80        3.0\n","3     30         140           85   7.0      98.0         70        3.0\n","4     35         120           60   6.1      98.0         76        1.0\n","..   ...         ...          ...   ...       ...        ...        ...\n","673   12         100           50   6.4      98.0         70        2.0\n","674   15         100           60   6.0      98.0         80        1.0\n","703   15         100           49   7.6      98.0         77        1.0\n","704   12         100           50   6.0      98.0         70        2.0\n","705   21         100           50   6.8      98.0         60        1.0\n","\n","[452 rows x 7 columns]"],"text/html":["\n","  <div id=\"df-1ad4b995-6a98-4336-8067-587443a651e1\" class=\"colab-df-container\">\n","    <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>Age</th>\n","      <th>SystolicBP</th>\n","      <th>DiastolicBP</th>\n","      <th>BS</th>\n","      <th>BodyTemp</th>\n","      <th>HeartRate</th>\n","      <th>RiskLevel</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>25</td>\n","      <td>130</td>\n","      <td>80</td>\n","      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Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","<div id=\"df-db885bcd-d9b5-4dfd-b074-b42df403a22e\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-db885bcd-d9b5-4dfd-b074-b42df403a22e')\"\n","            title=\"Suggest charts\"\n","            style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\n","  <script>\n","    async function quickchart(key) {\n","      const quickchartButtonEl =\n","        document.querySelector('#' + key + ' button');\n","      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n","      quickchartButtonEl.classList.add('colab-df-spinner');\n","      try {\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      } catch (error) {\n","        console.error('Error during call to suggestCharts:', error);\n","      }\n","      quickchartButtonEl.classList.remove('colab-df-spinner');\n","      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n","    }\n","    (() => {\n","      let quickchartButtonEl =\n","        document.querySelector('#df-db885bcd-d9b5-4dfd-b074-b42df403a22e button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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]\n}"}},"metadata":{},"execution_count":37}]},{"cell_type":"code","source":["import matplotlib.pyplot as plt\n","import seaborn as sns"],"metadata":{"id":"KyA5cAwJeE_r","executionInfo":{"status":"ok","timestamp":1715528565861,"user_tz":-330,"elapsed":2,"user":{"displayName":"Disha Mukherjee","userId":"03755156500668301044"}}},"execution_count":38,"outputs":[]},{"cell_type":"code","source":["import warnings\n","warnings.filterwarnings(\"ignore\")\n","\n","%matplotlib inline\n","plt.style.use(\"fivethirtyeight\")"],"metadata":{"id":"HmfGaTvseIgd","executionInfo":{"status":"ok","timestamp":1715528578977,"user_tz":-330,"elapsed":561,"user":{"displayName":"Disha Mukherjee","userId":"03755156500668301044"}}},"execution_count":39,"outputs":[]},{"cell_type":"code","source":["#data distribution for every column\n","plt.figure(figsize = (20,15))\n","plotnumber = 1\n","\n","for column in data:\n","    if plotnumber <= 7:\n","\n","        ax = plt.subplot(3,5,plotnumber)\n","        sns.distplot(data[column])\n","        plt.xlabel(column,fontsize=15)\n","\n","    plotnumber += 1\n","\n","plt.tight_layout()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":589},"id":"fPW7ayZHeLm1","executionInfo":{"status":"ok","timestamp":1715528611802,"user_tz":-330,"elapsed":4134,"user":{"displayName":"Disha Mukherjee","userId":"03755156500668301044"}},"outputId":"965b9576-e124-4f75-f909-824f59558e60"},"execution_count":40,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 2000x1500 with 7 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Mukherjee","userId":"03755156500668301044"}},"outputId":"17176570-2877-4f2c-94fc-9bb1e3edfa24"},"execution_count":41,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Index(['Age', 'SystolicBP', 'DiastolicBP', 'BS', 'BodyTemp', 'HeartRate',\n","       'RiskLevel'],\n","      dtype='object')"]},"metadata":{},"execution_count":41}]},{"cell_type":"code","source":["#column summary\n","\n","column_summary = {}\n","\n","# Approximate Distinct Count\n","column_summary['Approximate Distinct Count'] = data[\"Age\"].nunique()\n","\n","# Approximate Unique (%)\n","approx_unique_percentage = (column_summary['Approximate Distinct Count'] / len(data[\"Age\"])) * 100\n","column_summary['Approximate Unique (%)'] = f\"{approx_unique_percentage:.1f}%\"\n","\n","# Missing Values\n","column_summary[\"Missing\"] = data[\"Age\"].isnull().sum()\n","\n","# Missing (%)\n","missing_percentage = (column_summary['Missing'] / len(data[\"Age\"])) * 100\n","column_summary['Missing (%)'] = f\"{missing_percentage:.1f}%\"\n","\n","# Infinite Values (if applicable)\n","column_summary[\"Infinite\"] = data[\"Age\"].isin([float(\"inf\"),float(\"-inf\")]).sum()\n","\n","# Infinite (%)\n","infinite_percentage = (column_summary['Infinite'] / len(data[\"Age\"])) * 100\n","column_summary['Infinite (%)'] = f\"{infinite_percentage:.1f}%\"\n","\n","# Memory Size (assuming column is a Series)\n","column_summary['Memory Size'] = data['Age'].memory_usage(deep=True) / 1024  # in KB\n","\n","# Mean\n","column_summary['Mean'] = data[\"Age\"].mean()\n","\n","# Minimum\n","column_summary['Minimum'] = data[\"Age\"].min()\n","\n","# Maximum\n","column_summary['Maximum'] = data[\"Age\"].max()\n","\n","# Zeros\n","column_summary['Zeros'] = (data[\"Age\"] == 0).sum()\n","\n","# Zeros (%)\n","zeros_percentage = (column_summary['Zeros'] / len(data[\"Age\"])) * 100\n","column_summary['Zeros (%)'] = f\"{zeros_percentage:.1f}%\"\n","\n","# Negatives (if applicable)\n","column_summary['Negatives'] = (data[\"Age\"] < 0).sum()\n","\n","# Negatives (%)\n","negatives_percentage = (column_summary['Negatives'] / len(data[\"Age\"])) * 100\n","column_summary['Negatives (%)'] = f\"{negatives_percentage:.1f}%\"\n","\n","\n","\n","# Display the summary\n","for key, value in column_summary.items():\n","    print(f\"{key}\\t{value}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kRdFXcOyfHyr","executionInfo":{"status":"ok","timestamp":1715528847120,"user_tz":-330,"elapsed":549,"user":{"displayName":"Disha Mukherjee","userId":"03755156500668301044"}},"outputId":"8394171a-826c-40fb-889f-b7361a75d5ec"},"execution_count":42,"outputs":[{"output_type":"stream","name":"stdout","text":["Approximate Distinct Count\t50\n","Approximate Unique (%)\t11.1%\n","Missing\t0\n","Missing (%)\t0.0%\n","Infinite\t0\n","Infinite (%)\t0.0%\n","Memory Size\t7.0625\n","Mean\t29.194690265486727\n","Minimum\t10\n","Maximum\t70\n","Zeros\t0\n","Zeros (%)\t0.0%\n","Negatives\t0\n","Negatives (%)\t0.0%\n"]}]},{"cell_type":"markdown","source":["Descriptive Statictics"],"metadata":{"id":"iS8BC-nnfYDs"}},{"cell_type":"code","source":["from scipy.stats import skew, kurtosis"],"metadata":{"id":"XbI9uEsMfaWN","executionInfo":{"status":"ok","timestamp":1715529003800,"user_tz":-330,"elapsed":398,"user":{"displayName":"Disha Mukherjee","userId":"03755156500668301044"}}},"execution_count":43,"outputs":[]},{"cell_type":"code","source":["#Assuming we have an \"Age\" column in this data set stored in a numpy array called \"df\"\n","\n","df = np.array(data[\"Age\"])\n","\n","# Calculate Mean\n","mean = np.mean(df)\n","\n","# Calculate Standard Deviation\n","std_dev = np.std(df)\n","\n","# Calculate Variance\n","variance = np.var(df)\n","\n","# Calculate Sum\n","total_sum = np.sum(df)\n","\n","# Calculate Skewness\n","skewness = skew(df)\n","\n","# Calculate Kurtosis\n","kurt = kurtosis(df)\n","\n","# Calculate Coefficient of Variation\n","coef_variation = std_dev / mean\n","\n","# Print the results\n","print(\"Mean:\", mean)\n","print(\"Standard Deviation:\", std_dev)\n","print(\"Variance:\", variance)\n","print(\"Sum:\", total_sum)\n","print(\"Skewness:\", skewness)\n","print(\"Kurtosis:\", kurt)\n","print(\"Coefficient of Variation:\", coef_variation)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rwtnsFbtfzTb","executionInfo":{"status":"ok","timestamp":1715529029044,"user_tz":-330,"elapsed":406,"user":{"displayName":"Disha Mukherjee","userId":"03755156500668301044"}},"outputId":"2959bee2-6991-4652-d9b2-f134bc57114c"},"execution_count":44,"outputs":[{"output_type":"stream","name":"stdout","text":["Mean: 29.194690265486727\n","Standard Deviation: 13.752141205496798\n","Variance: 189.12138773592295\n","Sum: 13196\n","Skewness: 0.9190166099923903\n","Kurtosis: -0.10194052893537409\n","Coefficient of Variation: 0.4710493956414484\n"]}]},{"cell_type":"markdown","source":["Quantile Statictics"],"metadata":{"id":"E44SqJzhf-fh"}},{"cell_type":"code","source":["#Assuming we have an \"Age\" column in this data set stored in a numpy array called \"df\"\n","\n","df = np.array(data[\"Age\"])\n","\n","# Calculate Minimum\n","minimum = np.min(df)\n","\n","# Calculate 5th Percentile\n","percentile_5 = np.percentile(df, 5)\n","\n","# Calculate Q1 (First Quartile)\n","q1 = np.percentile(df, 25)\n","\n","# Calculate Median\n","median = np.median(df)\n","\n","# Calculate Q3 (Third Quartile)\n","q3 = np.percentile(df, 75)\n","\n","# Calculate 95th Percentile\n","percentile_95 = np.percentile(df, 95)\n","\n","# Calculate Maximum\n","maximum = np.max(df)\n","\n","# Calculate Range\n","data_range = maximum - minimum\n","\n","# Calculate Interquartile Range (IQR)\n","iqr = q3 - q1\n","\n","# Print the results\n","print(\"Minimum:\", minimum)\n","print(\"5th Percentile:\", percentile_5)\n","print(\"Q1 (First Quartile):\", q1)\n","print(\"Median:\", median)\n","print(\"Q3 (Third Quartile):\", q3)\n","print(\"95th Percentile:\", percentile_95)\n","print(\"Maximum:\", maximum)\n","print(\"Range:\", data_range)\n","print(\"IQR (Interquartile Range):\", iqr)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YWJEGxW_f6DY","executionInfo":{"status":"ok","timestamp":1715529077842,"user_tz":-330,"elapsed":389,"user":{"displayName":"Disha Mukherjee","userId":"03755156500668301044"}},"outputId":"c3f0e084-7b24-422a-80f6-cd6f1dda3d5f"},"execution_count":45,"outputs":[{"output_type":"stream","name":"stdout","text":["Minimum: 10\n","5th Percentile: 12.0\n","Q1 (First Quartile): 19.0\n","Median: 25.0\n","Q3 (Third Quartile): 35.0\n","95th Percentile: 59.44999999999999\n","Maximum: 70\n","Range: 60\n","IQR (Interquartile Range): 16.0\n"]}]},{"cell_type":"markdown","source":["Overview of risk level"],"metadata":{"id":"CLOFONmRgP4r"}},{"cell_type":"code","source":["# RiskLevel Column\n","\n","column_summary = {}\n","\n","# Approximate Distinct Count\n","column_summary['Approximate Distinct Count'] = data[\"RiskLevel\"].nunique()\n","\n","# Approximate Unique (%)\n","approx_unique_percentage = (column_summary['Approximate Distinct Count'] / len(data[\"RiskLevel\"])) * 100\n","column_summary['Approximate Unique (%)'] = f\"{approx_unique_percentage:.1f}%\"\n","\n","# Missing Values\n","column_summary[\"Missing\"] = data[\"RiskLevel\"].isnull().sum()\n","\n","# Missing (%)\n","missing_percentage = (column_summary['Missing'] / len(data[\"RiskLevel\"])) * 100\n","column_summary['Missing (%)'] = f\"{missing_percentage:.1f}%\"\n","\n","# Memory Size (assuming column is a Series)\n","column_summary['Memory Size'] = data['RiskLevel'].memory_usage(deep=True) / 1024  # in KB\n","\n","\n","# Display the summary\n","for key, value in column_summary.items():\n","    print(f\"{key}\\t{value}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2KJEYSg6gF5Y","executionInfo":{"status":"ok","timestamp":1715529339615,"user_tz":-330,"elapsed":416,"user":{"displayName":"Disha Mukherjee","userId":"03755156500668301044"}},"outputId":"581047f2-0a52-4435-a1bf-32025c816c45"},"execution_count":46,"outputs":[{"output_type":"stream","name":"stdout","text":["Approximate Distinct Count\t3\n","Approximate Unique (%)\t0.7%\n","Missing\t0\n","Missing (%)\t0.0%\n","Memory Size\t7.0625\n"]}]},{"cell_type":"markdown","source":["letter"],"metadata":{"id":"uQjzEgUFhJO-"}},{"cell_type":"code","source":["import collections"],"metadata":{"id":"sEZZmCq8hFyO","executionInfo":{"status":"ok","timestamp":1715529371680,"user_tz":-330,"elapsed":539,"user":{"displayName":"Disha Mukherjee","userId":"03755156500668301044"}}},"execution_count":47,"outputs":[]},{"cell_type":"code","source":["data1 = pd.read_csv(\"/content/drive/MyDrive/Maternal Health Risk Prediction(GSSOC'24)/Maternal Health Risk Data Set.csv\")"],"metadata":{"id":"ZcKHCHQ7hNYm","executionInfo":{"status":"ok","timestamp":1715529425886,"user_tz":-330,"elapsed":406,"user":{"displayName":"Disha Mukherjee","userId":"03755156500668301044"}}},"execution_count":48,"outputs":[]},{"cell_type":"code","source":["data1.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"0AOlWYuIhazx","executionInfo":{"status":"ok","timestamp":1715529439223,"user_tz":-330,"elapsed":7,"user":{"displayName":"Disha Mukherjee","userId":"03755156500668301044"}},"outputId":"241e7698-394a-47f4-9347-b1b449dd9d56"},"execution_count":49,"outputs":[{"output_type":"execute_result","data":{"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"],"text/html":["\n","  <div id=\"df-e16d1d24-5b21-4132-a672-2fe00ad374a8\" class=\"colab-df-container\">\n","    <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>Age</th>\n","      <th>SystolicBP</th>\n","      <th>DiastolicBP</th>\n","      <th>BS</th>\n","      <th>BodyTemp</th>\n","      <th>HeartRate</th>\n","      <th>RiskLevel</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>25</td>\n","      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