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import pandas as pd from sklearn.preprocessing import KBinsDiscretizer # Load CSV file (must be uploaded to OnlineGDB) df = pd.read_csv("exp2.csv") print("\n=== Initial Data Info ===") print(df.info()) # Remove unnecessary columns df = df.drop(["Roll No", "Name of the Student"], axis=1) # Handle Missing Values df["Age"] = df["Age"].fillna(df["Age"].median()) df["Marks"] = df["Marks"].fillna(df["Marks"].median()) df["Grade"] = df["Grade"].fillna(df["Grade"].mode()[0]) df["Status"] = df["Status"].fillna(df["Status"].mode()[0]) print("\nMissing Values After Cleaning:") print(df.isnull().sum()) # Remove Outliers (Cap Marks > 100) df.loc[df["Marks"] > 100, "Marks"] = 100 # Discretization kb = KBinsDiscretizer(n_bins=5, encode="ordinal", strategy="quantile") df["Marks_Bin"] = kb.fit_transform(df[["Marks"]]).astype(int) # Final Output print("\n=== Final Processed Dataset ===") print(df.head()) print("\n=== Final Dataset Info ===") print(df.info())
Roll No,Name of the Student,Age,Marks,Grade,Status 1,Aarav,19,77,B,Pass 2,Isha,19,67,C,Pass 3,Rohan,19,4,F,Fail 4,Kavya,19,60,C,Pass 5,Arjun,20,91,S,Pass 6,Neha,NaN,53,D,Pass 7,Siddharth,20,12,F,Fail 8,Pooja,20,76,B,Pass 9,Diya,19,63,C,Pass 10,Kunal,20,45,E,Pass 11,Varun,20,17,F,Fail 12,Ananya,19,54,D,Pass 13,Rahul,21,61,C,Pass 14,Tanvi,19,70,B,Pass 15,Aditya,19,79,NaN,Pass 16,Simran,20,97,S,Pass 17,Mohit,19,92,S,Pass 18,Riya,19,66,C,Pass 19,Devansh,19,89,A,Pass 20,Sneha,19,88,A,Pass 21,Saurabh,21,48,E,Pass 22,Aditi,21,9,F,NaN 23,Pranav,19,74,B,Pass 24,Shalini,20,71,B,Pass 25,Yash,18,96,S,Pass 26,Nandini,21,83,A,Pass 27,Abhishek,20,63,C,Pass 28,Megha,21,66,C,Pass 29,Rohit,20,71,B,Pass 30,Pritika,19,65,C,Pass 31,Vishal,20,86,A,Pass 32,Kajal,19,50,D,Pass 33,Sanjay,21,2,F,Fail 34,Palki,19,21,F,Fail 35,Harsh,19,85,A,Pass 36,Sanya,20,80,A,Pass 37,Manish,20,87,A,Pass 38,Avni,21,69,C,Pass 39,Karthik,19,78,B,Pass 40,Roshni,20,55,D,Pass 41,Uday,21,64,C,Pass 42,Preeti,19,67,C,Pass 43,Tarun,19,62,C,Pass 44,Amrita,19,75,B,Pass 45,Rakesh,20,56,D,Pass 46,Iram,19,33,F,Fail 47,Ashwin,20,10,F,Fail 48,Sonali,19,75,B,Pass 49,Nitin,20,62,C,Pass 50,Zoya,19,77,B,Pass 51,Viraj,21,83,A,Pass 52,Bhavya,19,82,A,Pass 53,Rajat,20,150,S,Pass 54,Farah,20,78,B,Pass 55,Shubham,20,58,D,Pass 56,Pankhuri,19,69,C,Pass 57,Keerthi,20,45,E,Pass 58,Sameer,19,100,S,Pass 59,Ankit,20,78,B,Pass 60,Noor,20,80,A,Pass 61,Kiran,20,74,B,Pass 62,Ayaan,20,87,A,Pass 63,Ishita,19,65,C,Pass 64,Teja,19,51,D,Pass

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