From 49c6087314e56470225f25a7a862712429f6af49 Mon Sep 17 00:00:00 2001
From: Tandrita Mukherjee <mtandrita05@gmail.com>
Date: Sun, 28 Dec 2025 11:02:29 +0530
Subject: [PATCH 1/9] Phase A: ML training pipeline with preprocessing

---
 .../data_preprocessing.cpython-313.pyc        |  Bin 0 -> 2016 bytes
 .../diabetes_pipeline/data_preprocessing.py   |   40 +
 .../dataset/kaggle_diabetes.csv               | 2001 +++++++++++++++++
 .../model/diabetes_model.pkl                  |  Bin 0 -> 560313 bytes
 .../diabetes_pipeline/model/scaler.pkl        |  Bin 0 -> 1111 bytes
 .../diabetes_pipeline/train.py                |   18 +
 6 files changed, 2059 insertions(+)
 create mode 100644 Diabetes Prediction [END 2 END]/diabetes_pipeline/__pycache__/data_preprocessing.cpython-313.pyc
 create mode 100644 Diabetes Prediction [END 2 END]/diabetes_pipeline/data_preprocessing.py
 create mode 100644 Diabetes Prediction [END 2 END]/diabetes_pipeline/dataset/kaggle_diabetes.csv
 create mode 100644 Diabetes Prediction [END 2 END]/diabetes_pipeline/model/diabetes_model.pkl
 create mode 100644 Diabetes Prediction [END 2 END]/diabetes_pipeline/model/scaler.pkl
 create mode 100644 Diabetes Prediction [END 2 END]/diabetes_pipeline/train.py

diff --git a/Diabetes Prediction [END 2 END]/diabetes_pipeline/__pycache__/data_preprocessing.cpython-313.pyc b/Diabetes Prediction [END 2 END]/diabetes_pipeline/__pycache__/data_preprocessing.cpython-313.pyc
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diff --git a/Diabetes Prediction [END 2 END]/diabetes_pipeline/data_preprocessing.py b/Diabetes Prediction [END 2 END]/diabetes_pipeline/data_preprocessing.py
new file mode 100644
index 0000000..dae55f3
--- /dev/null
+++ b/Diabetes Prediction [END 2 END]/diabetes_pipeline/data_preprocessing.py	
@@ -0,0 +1,40 @@
+# diabetes_pipeline/data_preprocessing.py
+
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+
+def load_and_preprocess(csv_path='dataset/kaggle_diabetes.csv', test_size=0.2, random_state=0):
+    # Load dataset
+    df = pd.read_csv(csv_path)
+    
+    # Rename column
+    df = df.rename(columns={'DiabetesPedigreeFunction': 'DPF'})
+    
+    # Replace 0s with NaN
+    cols_with_zero = ['Glucose','BloodPressure','SkinThickness','Insulin','BMI']
+    df[cols_with_zero] = df[cols_with_zero].replace(0, np.nan)
+    
+    # Fill NaNs
+    df['Glucose'].fillna(df['Glucose'].mean(), inplace=True)
+    df['BloodPressure'].fillna(df['BloodPressure'].mean(), inplace=True)
+    df['SkinThickness'].fillna(df['SkinThickness'].median(), inplace=True)
+    df['Insulin'].fillna(df['Insulin'].median(), inplace=True)
+    df['BMI'].fillna(df['BMI'].median(), inplace=True)
+    
+    # Features & Target
+    X = df.drop(columns='Outcome')
+    y = df['Outcome']
+    
+    # Train/Test Split
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=test_size, random_state=random_state
+    )
+    
+    # Feature Scaling
+    scaler = StandardScaler()
+    X_train_scaled = scaler.fit_transform(X_train)
+    X_test_scaled = scaler.transform(X_test)
+    
+    return X_train_scaled, X_test_scaled, y_train, y_test, scaler
diff --git a/Diabetes Prediction [END 2 END]/diabetes_pipeline/dataset/kaggle_diabetes.csv b/Diabetes Prediction [END 2 END]/diabetes_pipeline/dataset/kaggle_diabetes.csv
new file mode 100644
index 0000000..a4c9c3a
--- /dev/null
+++ b/Diabetes Prediction [END 2 END]/diabetes_pipeline/dataset/kaggle_diabetes.csv	
@@ -0,0 +1,2001 @@
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diff --git a/Diabetes Prediction [END 2 END]/diabetes_pipeline/model/diabetes_model.pkl b/Diabetes Prediction [END 2 END]/diabetes_pipeline/model/diabetes_model.pkl
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diff --git a/Diabetes Prediction [END 2 END]/diabetes_pipeline/model/scaler.pkl b/Diabetes Prediction [END 2 END]/diabetes_pipeline/model/scaler.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..13ff46e7554ad160145c5512fc48b3e2216c675f
GIT binary patch
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diff --git a/Diabetes Prediction [END 2 END]/diabetes_pipeline/train.py b/Diabetes Prediction [END 2 END]/diabetes_pipeline/train.py
new file mode 100644
index 0000000..49b0958
--- /dev/null
+++ b/Diabetes Prediction [END 2 END]/diabetes_pipeline/train.py	
@@ -0,0 +1,18 @@
+# diabetes_pipeline/train.py
+
+import joblib
+from sklearn.ensemble import RandomForestClassifier
+from data_preprocessing import load_and_preprocess
+
+# Load and preprocess
+X_train, X_test, y_train, y_test, scaler = load_and_preprocess()
+
+# Train Random Forest
+classifier = RandomForestClassifier(n_estimators=20, random_state=0)
+classifier.fit(X_train, y_train)
+
+# Save model & scaler
+joblib.dump(classifier, 'model/diabetes_model.pkl')
+joblib.dump(scaler, 'model/scaler.pkl')
+
+print("Model and scaler saved in 'model/' folder.")

From e7b33debd660baea08c4378189544d9b7352709d Mon Sep 17 00:00:00 2001
From: Tandrita Mukherjee <mtandrita05@gmail.com>
Date: Sun, 28 Dec 2025 11:05:02 +0530
Subject: [PATCH 2/9] Remove pycache and ignore generated files

---
 .../diabetes_pipeline/.gitignore               |   4 ++++
 .../data_preprocessing.cpython-313.pyc         | Bin 2016 -> 0 bytes
 .../diabetes_pipeline/model/diabetes_model.pkl | Bin 560313 -> 0 bytes
 .../diabetes_pipeline/model/scaler.pkl         | Bin 1111 -> 0 bytes
 4 files changed, 4 insertions(+)
 create mode 100644 Diabetes Prediction [END 2 END]/diabetes_pipeline/.gitignore
 delete mode 100644 Diabetes Prediction [END 2 END]/diabetes_pipeline/__pycache__/data_preprocessing.cpython-313.pyc
 delete mode 100644 Diabetes Prediction [END 2 END]/diabetes_pipeline/model/diabetes_model.pkl
 delete mode 100644 Diabetes Prediction [END 2 END]/diabetes_pipeline/model/scaler.pkl

diff --git a/Diabetes Prediction [END 2 END]/diabetes_pipeline/.gitignore b/Diabetes Prediction [END 2 END]/diabetes_pipeline/.gitignore
new file mode 100644
index 0000000..4ec23b6
--- /dev/null
+++ b/Diabetes Prediction [END 2 END]/diabetes_pipeline/.gitignore	
@@ -0,0 +1,4 @@
+__pycache__/
+*.pyc
+model/*.pkl
+.venv/
diff --git a/Diabetes Prediction [END 2 END]/diabetes_pipeline/__pycache__/data_preprocessing.cpython-313.pyc b/Diabetes Prediction [END 2 END]/diabetes_pipeline/__pycache__/data_preprocessing.cpython-313.pyc
deleted file mode 100644
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GIT binary patch
literal 0
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diff --git a/Diabetes Prediction [END 2 END]/diabetes_pipeline/model/diabetes_model.pkl b/Diabetes Prediction [END 2 END]/diabetes_pipeline/model/diabetes_model.pkl
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GIT binary patch
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diff --git a/Diabetes Prediction [END 2 END]/diabetes_pipeline/model/scaler.pkl b/Diabetes Prediction [END 2 END]/diabetes_pipeline/model/scaler.pkl
deleted file mode 100644
index 13ff46e7554ad160145c5512fc48b3e2216c675f..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

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From a8c862bf249f6e722296f274e5700e6a8166f0cc Mon Sep 17 00:00:00 2001
From: Tandrita Mukherjee <mtandrita05@gmail.com>
Date: Sun, 28 Dec 2025 11:34:27 +0530
Subject: [PATCH 3/9] Phase B: Add prediction module with config-based model
 loading

---
 .../diabetes_pipeline/config.py               |  7 +++++
 .../diabetes_pipeline/predict.py              | 30 +++++++++++++++++++
 .../diabetes_pipeline/train.py                | 16 ++++++----
 3 files changed, 47 insertions(+), 6 deletions(-)
 create mode 100644 Diabetes Prediction [END 2 END]/diabetes_pipeline/config.py
 create mode 100644 Diabetes Prediction [END 2 END]/diabetes_pipeline/predict.py

diff --git a/Diabetes Prediction [END 2 END]/diabetes_pipeline/config.py b/Diabetes Prediction [END 2 END]/diabetes_pipeline/config.py
new file mode 100644
index 0000000..47431de
--- /dev/null
+++ b/Diabetes Prediction [END 2 END]/diabetes_pipeline/config.py	
@@ -0,0 +1,7 @@
+from pathlib import Path
+
+BASE_DIR = Path(__file__).resolve().parent
+
+MODEL_DIR = BASE_DIR / "model"
+MODEL_PATH = MODEL_DIR / "diabetes_model.pkl"
+SCALER_PATH = MODEL_DIR / "scaler.pkl"
diff --git a/Diabetes Prediction [END 2 END]/diabetes_pipeline/predict.py b/Diabetes Prediction [END 2 END]/diabetes_pipeline/predict.py
new file mode 100644
index 0000000..0e859f5
--- /dev/null
+++ b/Diabetes Prediction [END 2 END]/diabetes_pipeline/predict.py	
@@ -0,0 +1,30 @@
+import joblib
+import numpy as np
+from config import MODEL_PATH, SCALER_PATH
+
+class DiabetesPredictor:
+	def __init__(self):
+		self.model = joblib.load(MODEL_PATH)
+		self.scaler = joblib.load(SCALER_PATH)
+
+	def predict(self, features: list) -> int:
+		"""
+		features order:
+		[Pregnancies, Glucose, BloodPressure, SkinThickness,
+		 Insulin, BMI, DPF, Age]
+		"""
+		features = np.array(features).reshape(1, -1)
+		features = self.scaler.transform(features)
+		return int(self.model.predict(features)[0])
+
+
+if __name__ == "__main__":
+	predictor = DiabetesPredictor()
+
+	sample_input = [2, 81, 72, 15, 76, 30.1, 0.547, 25]
+	result = predictor.predict(sample_input)
+
+	if result == 1:
+		print("Oops! You have diabetes.")
+	else:
+		print("Great! You don't have diabetes.")
diff --git a/Diabetes Prediction [END 2 END]/diabetes_pipeline/train.py b/Diabetes Prediction [END 2 END]/diabetes_pipeline/train.py
index 49b0958..875247d 100644
--- a/Diabetes Prediction [END 2 END]/diabetes_pipeline/train.py	
+++ b/Diabetes Prediction [END 2 END]/diabetes_pipeline/train.py	
@@ -3,16 +3,20 @@
 import joblib
 from sklearn.ensemble import RandomForestClassifier
 from data_preprocessing import load_and_preprocess
+from config import MODEL_DIR, MODEL_PATH, SCALER_PATH
 
-# Load and preprocess
+# Ensure model directory exists
+MODEL_DIR.mkdir(exist_ok=True)
+
+# Load and preprocess data
 X_train, X_test, y_train, y_test, scaler = load_and_preprocess()
 
-# Train Random Forest
+# Train model
 classifier = RandomForestClassifier(n_estimators=20, random_state=0)
 classifier.fit(X_train, y_train)
 
-# Save model & scaler
-joblib.dump(classifier, 'model/diabetes_model.pkl')
-joblib.dump(scaler, 'model/scaler.pkl')
+# Save model and scaler
+joblib.dump(classifier, MODEL_PATH)
+joblib.dump(scaler, SCALER_PATH)
 
-print("Model and scaler saved in 'model/' folder.")
+print("Model and scaler saved successfully.")

From 23b9d6f11bd8c267525f92b15800ca12d0b4bc63 Mon Sep 17 00:00:00 2001
From: Tandrita Mukherjee <mtandrita05@gmail.com>
Date: Sun, 28 Dec 2025 11:39:54 +0530
Subject: [PATCH 4/9] Fix pandas chained assignment warnings

---
 .../dataset/kaggle_diabetes.csv               | 2001 -----------------
 .../diabetes_pipeline/data_preprocessing.py   |   10 +-
 2 files changed, 5 insertions(+), 2006 deletions(-)
 delete mode 100644 Diabetes Prediction [END 2 END]/dataset/kaggle_diabetes.csv

diff --git a/Diabetes Prediction [END 2 END]/dataset/kaggle_diabetes.csv b/Diabetes Prediction [END 2 END]/dataset/kaggle_diabetes.csv
deleted file mode 100644
index a4c9c3a..0000000
--- a/Diabetes Prediction [END 2 END]/dataset/kaggle_diabetes.csv	
+++ /dev/null
@@ -1,2001 +0,0 @@
-Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age,Outcome
-2,138,62,35,0,33.6,0.127,47,1
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-0,173,78,32,265,46.5,1.159,58,0
-4,99,72,17,0,25.6,0.294,28,0
-8,194,80,0,0,26.1,0.551,67,0
-2,83,65,28,66,36.8,0.629,24,0
-2,89,90,30,0,33.5,0.292,42,0
-4,99,68,38,0,32.8,0.145,33,0
-4,125,70,18,122,28.9,1.144,45,1
-3,80,0,0,0,0,0.174,22,0
-6,166,74,0,0,26.6,0.304,66,0
-5,110,68,0,0,26,0.292,30,0
-2,81,72,15,76,30.1,0.547,25,0
-7,195,70,33,145,25.1,0.163,55,1
-6,154,74,32,193,29.3,0.839,39,0
-2,117,90,19,71,25.2,0.313,21,0
-3,84,72,32,0,37.2,0.267,28,0
-6,0,68,41,0,39,0.727,41,1
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-3,96,78,39,0,37.3,0.238,40,0
-10,75,82,0,0,33.3,0.263,38,0
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-1,111,94,0,0,32.8,0.265,45,0
-4,112,78,40,0,39.4,0.236,38,0
-1,116,78,29,180,36.1,0.496,25,0
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-2,175,88,0,0,22.9,0.326,22,0
-2,92,52,0,0,30.1,0.141,22,0
-3,130,78,23,79,28.4,0.323,34,1
-8,120,86,0,0,28.4,0.259,22,1
-2,174,88,37,120,44.5,0.646,24,1
-2,106,56,27,165,29,0.426,22,0
-2,105,75,0,0,23.3,0.56,53,0
-4,95,60,32,0,35.4,0.284,28,0
-0,126,86,27,120,27.4,0.515,21,0
-8,65,72,23,0,32,0.6,42,0
-2,99,60,17,160,36.6,0.453,21,0
-1,102,74,0,0,39.5,0.293,42,1
-11,120,80,37,150,42.3,0.785,48,1
-3,102,44,20,94,30.8,0.4,26,0
-1,109,58,18,116,28.5,0.219,22,0
-9,140,94,0,0,32.7,0.734,45,1
-13,153,88,37,140,40.6,1.174,39,0
-12,100,84,33,105,30,0.488,46,0
-1,147,94,41,0,49.3,0.358,27,1
-1,81,74,41,57,46.3,1.096,32,0
-3,187,70,22,200,36.4,0.408,36,1
-6,162,62,0,0,24.3,0.178,50,1
-4,136,70,0,0,31.2,1.182,22,1
-1,121,78,39,74,39,0.261,28,0
-3,108,62,24,0,26,0.223,25,0
-0,181,88,44,510,43.3,0.222,26,1
-8,154,78,32,0,32.4,0.443,45,1
-1,128,88,39,110,36.5,1.057,37,1
-7,137,90,41,0,32,0.391,39,0
-0,123,72,0,0,36.3,0.258,52,1
-1,106,76,0,0,37.5,0.197,26,0
-6,190,92,0,0,35.5,0.278,66,1
-2,88,58,26,16,28.4,0.766,22,0
-9,170,74,31,0,44,0.403,43,1
-9,89,62,0,0,22.5,0.142,33,0
-10,101,76,48,180,32.9,0.171,63,0
-2,122,70,27,0,36.8,0.34,27,0
-5,121,72,23,112,26.2,0.245,30,0
-1,126,60,0,0,30.1,0.349,47,1
-10,122,78,31,0,27.6,0.512,45,0
-4,103,60,33,192,24,0.966,33,0
-11,138,76,0,0,33.2,0.42,35,0
-9,102,76,37,0,32.9,0.665,46,1
-2,90,68,42,0,38.2,0.503,27,1
-4,111,72,47,207,37.1,1.39,56,1
-3,180,64,25,70,34,0.271,26,0
-7,133,84,0,0,40.2,0.696,37,0
-7,106,92,18,0,22.7,0.235,48,0
-9,171,110,24,240,45.4,0.721,54,1
-7,159,64,0,0,27.4,0.294,40,0
-0,180,66,39,0,42,1.893,25,1
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-7,103,66,32,0,39.1,0.344,31,1
-7,105,0,0,0,0,0.305,24,0
-1,103,80,11,82,19.4,0.491,22,0
-1,101,50,15,36,24.2,0.526,26,0
-5,88,66,21,23,24.4,0.342,30,0
-8,176,90,34,300,33.7,0.467,58,1
-7,150,66,42,342,34.7,0.718,42,0
-1,73,50,10,0,23,0.248,21,0
-7,187,68,39,304,37.7,0.254,41,1
-0,100,88,60,110,46.8,0.962,31,0
-0,146,82,0,0,40.5,1.781,44,0
-0,105,64,41,142,41.5,0.173,22,0
-2,84,0,0,0,0,0.304,21,0
-8,133,72,0,0,32.9,0.27,39,1
-5,44,62,0,0,25,0.587,36,0
-2,141,58,34,128,25.4,0.699,24,0
-7,114,66,0,0,32.8,0.258,42,1
-5,99,74,27,0,29,0.203,32,0
-0,109,88,30,0,32.5,0.855,38,1
-2,109,92,0,0,42.7,0.845,54,0
-1,95,66,13,38,19.6,0.334,25,0
-4,146,85,27,100,28.9,0.189,27,0
-2,100,66,20,90,32.9,0.867,28,1
-5,139,64,35,140,28.6,0.411,26,0
-13,126,90,0,0,43.4,0.583,42,1
-4,129,86,20,270,35.1,0.231,23,0
-1,79,75,30,0,32,0.396,22,0
-1,0,48,20,0,24.7,0.14,22,0
-7,62,78,0,0,32.6,0.391,41,0
-5,95,72,33,0,37.7,0.37,27,0
-0,131,0,0,0,43.2,0.27,26,1
-2,112,66,22,0,25,0.307,24,0
-3,113,44,13,0,22.4,0.14,22,0
-2,74,0,0,0,0,0.102,22,0
-7,83,78,26,71,29.3,0.767,36,0
-0,101,65,28,0,24.6,0.237,22,0
-5,137,108,0,0,48.8,0.227,37,1
-2,110,74,29,125,32.4,0.698,27,0
-13,106,72,54,0,36.6,0.178,45,0
-2,100,68,25,71,38.5,0.324,26,0
-15,136,70,32,110,37.1,0.153,43,1
-1,107,68,19,0,26.5,0.165,24,0
-1,80,55,0,0,19.1,0.258,21,0
-4,123,80,15,176,32,0.443,34,0
-7,81,78,40,48,46.7,0.261,42,0
-4,134,72,0,0,23.8,0.277,60,1
-2,142,82,18,64,24.7,0.761,21,0
-6,144,72,27,228,33.9,0.255,40,0
-2,92,62,28,0,31.6,0.13,24,0
-1,71,48,18,76,20.4,0.323,22,0
-6,93,50,30,64,28.7,0.356,23,0
-1,122,90,51,220,49.7,0.325,31,1
-1,163,72,0,0,39,1.222,33,1
-1,151,60,0,0,26.1,0.179,22,0
-0,125,96,0,0,22.5,0.262,21,0
-1,81,72,18,40,26.6,0.283,24,0
-2,85,65,0,0,39.6,0.93,27,0
-1,126,56,29,152,28.7,0.801,21,0
-1,96,122,0,0,22.4,0.207,27,0
-4,144,58,28,140,29.5,0.287,37,0
-3,83,58,31,18,34.3,0.336,25,0
-0,95,85,25,36,37.4,0.247,24,1
-3,171,72,33,135,33.3,0.199,24,1
-8,155,62,26,495,34,0.543,46,1
-1,89,76,34,37,31.2,0.192,23,0
-4,76,62,0,0,34,0.391,25,0
-7,160,54,32,175,30.5,0.588,39,1
-4,146,92,0,0,31.2,0.539,61,1
-5,124,74,0,0,34,0.22,38,1
-5,78,48,0,0,33.7,0.654,25,0
-4,97,60,23,0,28.2,0.443,22,0
-4,99,76,15,51,23.2,0.223,21,0
-0,162,76,56,100,53.2,0.759,25,1
-6,111,64,39,0,34.2,0.26,24,0
-2,107,74,30,100,33.6,0.404,23,0
-5,132,80,0,0,26.8,0.186,69,0
-0,113,76,0,0,33.3,0.278,23,1
-1,88,30,42,99,55,0.496,26,1
-3,120,70,30,135,42.9,0.452,30,0
-1,118,58,36,94,33.3,0.261,23,0
-1,117,88,24,145,34.5,0.403,40,1
-0,105,84,0,0,27.9,0.741,62,1
-4,173,70,14,168,29.7,0.361,33,1
-9,122,56,0,0,33.3,1.114,33,1
-3,170,64,37,225,34.5,0.356,30,1
-8,84,74,31,0,38.3,0.457,39,0
-2,96,68,13,49,21.1,0.647,26,0
-2,125,60,20,140,33.8,0.088,31,0
-0,100,70,26,50,30.8,0.597,21,0
-0,93,60,25,92,28.7,0.532,22,0
-0,129,80,0,0,31.2,0.703,29,0
-5,105,72,29,325,36.9,0.159,28,0
-3,128,78,0,0,21.1,0.268,55,0
-5,106,82,30,0,39.5,0.286,38,0
-2,108,52,26,63,32.5,0.318,22,0
-10,108,66,0,0,32.4,0.272,42,1
-4,154,62,31,284,32.8,0.237,23,0
-0,102,75,23,0,0,0.572,21,0
-9,57,80,37,0,32.8,0.096,41,0
-2,106,64,35,119,30.5,1.4,34,0
-5,147,78,0,0,33.7,0.218,65,0
-2,90,70,17,0,27.3,0.085,22,0
-1,136,74,50,204,37.4,0.399,24,0
-4,114,65,0,0,21.9,0.432,37,0
-9,156,86,28,155,34.3,1.189,42,1
-1,153,82,42,485,40.6,0.687,23,0
-8,188,78,0,0,47.9,0.137,43,1
-7,152,88,44,0,50,0.337,36,1
-2,99,52,15,94,24.6,0.637,21,0
-1,109,56,21,135,25.2,0.833,23,0
-2,88,74,19,53,29,0.229,22,0
-17,163,72,41,114,40.9,0.817,47,1
-4,151,90,38,0,29.7,0.294,36,0
-7,102,74,40,105,37.2,0.204,45,0
-0,114,80,34,285,44.2,0.167,27,0
-2,100,64,23,0,29.7,0.368,21,0
-0,131,88,0,0,31.6,0.743,32,1
-6,104,74,18,156,29.9,0.722,41,1
-3,148,66,25,0,32.5,0.256,22,0
-4,120,68,0,0,29.6,0.709,34,0
-4,110,66,0,0,31.9,0.471,29,0
-3,111,90,12,78,28.4,0.495,29,0
-6,102,82,0,0,30.8,0.18,36,1
-6,134,70,23,130,35.4,0.542,29,1
-2,87,0,23,0,28.9,0.773,25,0
-1,79,60,42,48,43.5,0.678,23,0
-2,75,64,24,55,29.7,0.37,33,0
-8,179,72,42,130,32.7,0.719,36,1
-6,85,78,0,0,31.2,0.382,42,0
-0,129,110,46,130,67.1,0.319,26,1
-2,81,72,15,76,30.1,0.547,25,0
diff --git a/Diabetes Prediction [END 2 END]/diabetes_pipeline/data_preprocessing.py b/Diabetes Prediction [END 2 END]/diabetes_pipeline/data_preprocessing.py
index dae55f3..97558ac 100644
--- a/Diabetes Prediction [END 2 END]/diabetes_pipeline/data_preprocessing.py	
+++ b/Diabetes Prediction [END 2 END]/diabetes_pipeline/data_preprocessing.py	
@@ -17,11 +17,11 @@ def load_and_preprocess(csv_path='dataset/kaggle_diabetes.csv', test_size=0.2, r
     df[cols_with_zero] = df[cols_with_zero].replace(0, np.nan)
     
     # Fill NaNs
-    df['Glucose'].fillna(df['Glucose'].mean(), inplace=True)
-    df['BloodPressure'].fillna(df['BloodPressure'].mean(), inplace=True)
-    df['SkinThickness'].fillna(df['SkinThickness'].median(), inplace=True)
-    df['Insulin'].fillna(df['Insulin'].median(), inplace=True)
-    df['BMI'].fillna(df['BMI'].median(), inplace=True)
+    df['Glucose'] = df['Glucose'].fillna(df['Glucose'].mean())
+    df['BloodPressure'] = df['BloodPressure'].fillna(df['BloodPressure'].mean())
+    df['SkinThickness'] = df['SkinThickness'].fillna(df['SkinThickness'].median())
+    df['Insulin'] = df['Insulin'].fillna(df['Insulin'].median())
+    df['BMI'] = df['BMI'].fillna(df['BMI'].median())
     
     # Features & Target
     X = df.drop(columns='Outcome')

From 77d454b57f6b9e048bda69852ff6b841cbf312f9 Mon Sep 17 00:00:00 2001
From: Tandrita Mukherjee <mtandrita05@gmail.com>
Date: Sun, 28 Dec 2025 12:15:17 +0530
Subject: [PATCH 5/9] Add evaluation script and logs for Phase C

---
 .../diabetes_pipeline/evaluate.py             | 20 +++++++++++++++++++
 .../diabetes_pipeline/logs/training.log       |  4 ++++
 2 files changed, 24 insertions(+)
 create mode 100644 Diabetes Prediction [END 2 END]/diabetes_pipeline/evaluate.py
 create mode 100644 Diabetes Prediction [END 2 END]/diabetes_pipeline/logs/training.log

diff --git a/Diabetes Prediction [END 2 END]/diabetes_pipeline/evaluate.py b/Diabetes Prediction [END 2 END]/diabetes_pipeline/evaluate.py
new file mode 100644
index 0000000..e8e8bd8
--- /dev/null
+++ b/Diabetes Prediction [END 2 END]/diabetes_pipeline/evaluate.py	
@@ -0,0 +1,20 @@
+import joblib
+from sklearn.metrics import accuracy_score, classification_report
+from data_preprocessing import load_and_preprocess
+from config import MODEL_PATH
+
+# Load data
+X_train, X_test, y_train, y_test, _ = load_and_preprocess()
+
+# Load trained model
+model = joblib.load(MODEL_PATH)
+
+# Predict
+y_pred = model.predict(X_test)
+
+# Metrics
+accuracy = accuracy_score(y_test, y_pred)
+report = classification_report(y_test, y_pred)
+
+print("Accuracy:", accuracy)
+print("\nClassification Report:\n", report)
diff --git a/Diabetes Prediction [END 2 END]/diabetes_pipeline/logs/training.log b/Diabetes Prediction [END 2 END]/diabetes_pipeline/logs/training.log
new file mode 100644
index 0000000..53025b1
--- /dev/null
+++ b/Diabetes Prediction [END 2 END]/diabetes_pipeline/logs/training.log	
@@ -0,0 +1,4 @@
+2025-12-28 11:48:56,518 - INFO - Training started
+2025-12-28 11:48:56,641 - INFO - Model and scaler saved successfully
+2025-12-28 11:49:14,730 - INFO - Training started
+2025-12-28 11:49:14,821 - INFO - Model and scaler saved successfully

From b6d54af3cb881586b1173216eea17bdaf9190509 Mon Sep 17 00:00:00 2001
From: Tandrita Mukherjee <mtandrita05@gmail.com>
Date: Sun, 28 Dec 2025 12:16:48 +0530
Subject: [PATCH 6/9] Update train.py and predict.py with new preprocessing and
 prediction logic

---
 .../diabetes_pipeline/predict.py              | 73 +++++++++++--------
 .../diabetes_pipeline/train.py                | 23 ++++--
 2 files changed, 59 insertions(+), 37 deletions(-)

diff --git a/Diabetes Prediction [END 2 END]/diabetes_pipeline/predict.py b/Diabetes Prediction [END 2 END]/diabetes_pipeline/predict.py
index 0e859f5..b83a64e 100644
--- a/Diabetes Prediction [END 2 END]/diabetes_pipeline/predict.py	
+++ b/Diabetes Prediction [END 2 END]/diabetes_pipeline/predict.py	
@@ -1,30 +1,45 @@
+import argparse
 import joblib
-import numpy as np
-from config import MODEL_PATH, SCALER_PATH
-
-class DiabetesPredictor:
-	def __init__(self):
-		self.model = joblib.load(MODEL_PATH)
-		self.scaler = joblib.load(SCALER_PATH)
-
-	def predict(self, features: list) -> int:
-		"""
-		features order:
-		[Pregnancies, Glucose, BloodPressure, SkinThickness,
-		 Insulin, BMI, DPF, Age]
-		"""
-		features = np.array(features).reshape(1, -1)
-		features = self.scaler.transform(features)
-		return int(self.model.predict(features)[0])
-
-
-if __name__ == "__main__":
-	predictor = DiabetesPredictor()
-
-	sample_input = [2, 81, 72, 15, 76, 30.1, 0.547, 25]
-	result = predictor.predict(sample_input)
-
-	if result == 1:
-		print("Oops! You have diabetes.")
-	else:
-		print("Great! You don't have diabetes.")
+import pandas as pd
+
+MODEL_PATH = "model/diabetes_model.pkl"
+SCALER_PATH = "model/scaler.pkl"
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--pregnancies", type=int, required=True)
+parser.add_argument("--glucose", type=float, required=True)
+parser.add_argument("--bp", type=float, required=True)
+parser.add_argument("--skin", type=float, required=True)
+parser.add_argument("--insulin", type=float, required=True)
+parser.add_argument("--bmi", type=float, required=True)
+parser.add_argument("--dpf", type=float, required=True)
+parser.add_argument("--age", type=int, required=True)
+
+args = parser.parse_args()
+
+# Load model & scaler
+model = joblib.load(MODEL_PATH)
+scaler = joblib.load(SCALER_PATH)
+
+# IMPORTANT: feature names must match training
+input_data = pd.DataFrame([{
+	"Pregnancies": args.pregnancies,
+	"Glucose": args.glucose,
+	"BloodPressure": args.bp,
+	"SkinThickness": args.skin,
+	"Insulin": args.insulin,
+	"BMI": args.bmi,
+	"DPF": args.dpf,
+	"Age": args.age
+}])
+
+# Scale & predict
+input_scaled = scaler.transform(input_data)
+prediction = model.predict(input_scaled)[0]
+
+if prediction == 1:
+	print("⚠️ Diabetes detected")
+else:
+	print("✅ No diabetes detected")
+
+
diff --git a/Diabetes Prediction [END 2 END]/diabetes_pipeline/train.py b/Diabetes Prediction [END 2 END]/diabetes_pipeline/train.py
index 875247d..c84e53c 100644
--- a/Diabetes Prediction [END 2 END]/diabetes_pipeline/train.py	
+++ b/Diabetes Prediction [END 2 END]/diabetes_pipeline/train.py	
@@ -1,22 +1,29 @@
-# diabetes_pipeline/train.py
-
+import logging
 import joblib
 from sklearn.ensemble import RandomForestClassifier
 from data_preprocessing import load_and_preprocess
-from config import MODEL_DIR, MODEL_PATH, SCALER_PATH
+from config import MODEL_PATH, SCALER_PATH, MODEL_DIR
 
-# Ensure model directory exists
-MODEL_DIR.mkdir(exist_ok=True)
+# Logging setup
+logging.basicConfig(
+	filename="logs/training.log",
+	level=logging.INFO,
+	format="%(asctime)s - %(levelname)s - %(message)s"
+)
+
+logging.info("Training started")
 
-# Load and preprocess data
+# Load data
 X_train, X_test, y_train, y_test, scaler = load_and_preprocess()
 
 # Train model
 classifier = RandomForestClassifier(n_estimators=20, random_state=0)
 classifier.fit(X_train, y_train)
 
-# Save model and scaler
+# Save artifacts
+MODEL_DIR.mkdir(exist_ok=True)
 joblib.dump(classifier, MODEL_PATH)
 joblib.dump(scaler, SCALER_PATH)
 
-print("Model and scaler saved successfully.")
+logging.info("Model and scaler saved successfully")
+

From 95338e01d407d7f1ab7eda45895485c61feb4e9b Mon Sep 17 00:00:00 2001
From: Tandrita Mukherjee <mtandrita05@gmail.com>
Date: Thu, 1 Jan 2026 11:36:56 +0530
Subject: [PATCH 7/9] Fix pandas chained assignment warnings

---
 .../diabetes_pipeline/data_preprocessing.py   | 33 +++++++++++--------
 1 file changed, 20 insertions(+), 13 deletions(-)

diff --git a/Diabetes Prediction [END 2 END]/diabetes_pipeline/data_preprocessing.py b/Diabetes Prediction [END 2 END]/diabetes_pipeline/data_preprocessing.py
index 97558ac..4348ffc 100644
--- a/Diabetes Prediction [END 2 END]/diabetes_pipeline/data_preprocessing.py	
+++ b/Diabetes Prediction [END 2 END]/diabetes_pipeline/data_preprocessing.py	
@@ -2,39 +2,46 @@
 
 import pandas as pd
 import numpy as np
+from pathlib import Path
 from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import StandardScaler
 
-def load_and_preprocess(csv_path='dataset/kaggle_diabetes.csv', test_size=0.2, random_state=0):
+BASE_DIR = Path(__file__).resolve().parent
+
+def load_and_preprocess(
+    csv_path=BASE_DIR / "dataset" / "kaggle_diabetes.csv",
+    test_size=0.2,
+    random_state=0
+):
     # Load dataset
     df = pd.read_csv(csv_path)
-    
-    # Rename column
+
+    # Rename column (IMPORTANT: this defines feature names forever)
     df = df.rename(columns={'DiabetesPedigreeFunction': 'DPF'})
-    
-    # Replace 0s with NaN
+
+    # Replace invalid zeros
     cols_with_zero = ['Glucose','BloodPressure','SkinThickness','Insulin','BMI']
     df[cols_with_zero] = df[cols_with_zero].replace(0, np.nan)
-    
+
     # Fill NaNs
     df['Glucose'] = df['Glucose'].fillna(df['Glucose'].mean())
     df['BloodPressure'] = df['BloodPressure'].fillna(df['BloodPressure'].mean())
     df['SkinThickness'] = df['SkinThickness'].fillna(df['SkinThickness'].median())
     df['Insulin'] = df['Insulin'].fillna(df['Insulin'].median())
     df['BMI'] = df['BMI'].fillna(df['BMI'].median())
-    
-    # Features & Target
+
+    # Features & target
     X = df.drop(columns='Outcome')
     y = df['Outcome']
-    
-    # Train/Test Split
+
+    # Split
     X_train, X_test, y_train, y_test = train_test_split(
         X, y, test_size=test_size, random_state=random_state
     )
-    
-    # Feature Scaling
+
+    # Scaling
     scaler = StandardScaler()
     X_train_scaled = scaler.fit_transform(X_train)
     X_test_scaled = scaler.transform(X_test)
-    
+
     return X_train_scaled, X_test_scaled, y_train, y_test, scaler

From 15e3398854463b42822acc37a696225571af6fc3 Mon Sep 17 00:00:00 2001
From: Tandrita Mukherjee <mtandrita05@gmail.com>
Date: Thu, 1 Jan 2026 11:43:26 +0530
Subject: [PATCH 8/9] Phase C: Fix data leakage using sklearn pipelines

---
 .../diabetes_pipeline/data_preprocessing.py   | 26 ++---------
 .../diabetes_pipeline/experiments/__init__.py |  0
 .../experiments/experiment_runner.py          | 43 +++++++++++++++++++
 .../diabetes_pipeline/experiments/results.csv |  5 +++
 4 files changed, 52 insertions(+), 22 deletions(-)
 create mode 100644 Diabetes Prediction [END 2 END]/diabetes_pipeline/experiments/__init__.py
 create mode 100644 Diabetes Prediction [END 2 END]/diabetes_pipeline/experiments/experiment_runner.py
 create mode 100644 Diabetes Prediction [END 2 END]/diabetes_pipeline/experiments/results.csv

diff --git a/Diabetes Prediction [END 2 END]/diabetes_pipeline/data_preprocessing.py b/Diabetes Prediction [END 2 END]/diabetes_pipeline/data_preprocessing.py
index 4348ffc..4c5f805 100644
--- a/Diabetes Prediction [END 2 END]/diabetes_pipeline/data_preprocessing.py	
+++ b/Diabetes Prediction [END 2 END]/diabetes_pipeline/data_preprocessing.py	
@@ -4,44 +4,26 @@
 import numpy as np
 from pathlib import Path
 from sklearn.model_selection import train_test_split
-from sklearn.preprocessing import StandardScaler
 
-BASE_DIR = Path(__file__).resolve().parent
-
-def load_and_preprocess(
-    csv_path=BASE_DIR / "dataset" / "kaggle_diabetes.csv",
-    test_size=0.2,
-    random_state=0
-):
-    # Load dataset
+def load_and_preprocess(test_size=0.2, random_state=0):
+    BASE_DIR = Path(__file__).resolve().parent
+    csv_path = BASE_DIR / "dataset" / "kaggle_diabetes.csv"
     df = pd.read_csv(csv_path)
 
-    # Rename column (IMPORTANT: this defines feature names forever)
     df = df.rename(columns={'DiabetesPedigreeFunction': 'DPF'})
 
-    # Replace invalid zeros
     cols_with_zero = ['Glucose','BloodPressure','SkinThickness','Insulin','BMI']
     df[cols_with_zero] = df[cols_with_zero].replace(0, np.nan)
 
-    # Fill NaNs
     df['Glucose'] = df['Glucose'].fillna(df['Glucose'].mean())
     df['BloodPressure'] = df['BloodPressure'].fillna(df['BloodPressure'].mean())
     df['SkinThickness'] = df['SkinThickness'].fillna(df['SkinThickness'].median())
     df['Insulin'] = df['Insulin'].fillna(df['Insulin'].median())
     df['BMI'] = df['BMI'].fillna(df['BMI'].median())
 
-    # Features & target
     X = df.drop(columns='Outcome')
     y = df['Outcome']
 
-    # Split
-    X_train, X_test, y_train, y_test = train_test_split(
+    return train_test_split(
         X, y, test_size=test_size, random_state=random_state
     )
-
-    # Scaling
-    scaler = StandardScaler()
-    X_train_scaled = scaler.fit_transform(X_train)
-    X_test_scaled = scaler.transform(X_test)
-
-    return X_train_scaled, X_test_scaled, y_train, y_test, scaler
diff --git a/Diabetes Prediction [END 2 END]/diabetes_pipeline/experiments/__init__.py b/Diabetes Prediction [END 2 END]/diabetes_pipeline/experiments/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/Diabetes Prediction [END 2 END]/diabetes_pipeline/experiments/experiment_runner.py b/Diabetes Prediction [END 2 END]/diabetes_pipeline/experiments/experiment_runner.py
new file mode 100644
index 0000000..afcf9a2
--- /dev/null
+++ b/Diabetes Prediction [END 2 END]/diabetes_pipeline/experiments/experiment_runner.py	
@@ -0,0 +1,43 @@
+# diabetes_pipeline/experiments/experiment_runner.py
+
+import pandas as pd
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import StandardScaler
+from sklearn.linear_model import LogisticRegression
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.svm import SVC
+from sklearn.metrics import accuracy_score, f1_score
+
+from diabetes_pipeline.data_preprocessing import load_and_preprocess
+
+X_train, X_test, y_train, y_test = load_and_preprocess()
+
+models = {
+	"LogisticRegression": LogisticRegression(max_iter=1000),
+	"DecisionTree": DecisionTreeClassifier(random_state=0),
+	"RandomForest": RandomForestClassifier(n_estimators=50, random_state=0),
+	"SVM": SVC()
+}
+
+results = []
+
+for name, model in models.items():
+	pipeline = Pipeline([
+		("scaler", StandardScaler()),
+		("model", model)
+	])
+
+	pipeline.fit(X_train, y_train)
+	preds = pipeline.predict(X_test)
+
+	results.append({
+		"Model": name,
+		"Accuracy": accuracy_score(y_test, preds),
+		"F1 Score": f1_score(y_test, preds)
+	})
+
+df = pd.DataFrame(results)
+print(df)
+
+df.to_csv("diabetes_pipeline/experiments/results.csv", index=False)
diff --git a/Diabetes Prediction [END 2 END]/diabetes_pipeline/experiments/results.csv b/Diabetes Prediction [END 2 END]/diabetes_pipeline/experiments/results.csv
new file mode 100644
index 0000000..6c2e471
--- /dev/null
+++ b/Diabetes Prediction [END 2 END]/diabetes_pipeline/experiments/results.csv	
@@ -0,0 +1,5 @@
+Model,Accuracy,F1 Score
+LogisticRegression,0.7875,0.6320346320346321
+DecisionTree,0.9875,0.980544747081712
+RandomForest,0.995,0.9921259842519685
+SVM,0.845,0.7327586206896551

From 95b072c7ce712076cf3b2ee8f4d03876c5d3450c Mon Sep 17 00:00:00 2001
From: Tandrita Mukherjee <mtandrita05@gmail.com>
Date: Thu, 1 Jan 2026 18:41:39 +0530
Subject: [PATCH 9/9] Add detailed README for ML pipeline contribution

---
 .../diabetes_pipeline/README.md               | 152 ++++++++++++++++++
 1 file changed, 152 insertions(+)
 create mode 100644 Diabetes Prediction [END 2 END]/diabetes_pipeline/README.md

diff --git a/Diabetes Prediction [END 2 END]/diabetes_pipeline/README.md b/Diabetes Prediction [END 2 END]/diabetes_pipeline/README.md
new file mode 100644
index 0000000..c43b6d4
--- /dev/null
+++ b/Diabetes Prediction [END 2 END]/diabetes_pipeline/README.md	
@@ -0,0 +1,152 @@
+# Diabetes Prediction – Machine Learning Pipeline
+
+> ⚠️ This repository is a **forked project**.  
+> The work below represents my **independent contribution and extension** to the original codebase.
+
+This project implements a complete **end-to-end machine learning pipeline** for predicting diabetes using the Pima Indians Diabetes dataset.  
+The pipeline covers **data preprocessing, model training, evaluation, experimentation, and inference via CLI**.
+
+---
+
+## 📁 Project Structure
+diabetes_pipeline/
+│
+├── dataset/
+│ └── kaggle_diabetes.csv
+│
+├── model/
+│ ├── diabetes_model.pkl
+│ └── scaler.pkl
+│
+├── experiments/
+│ └── experiment_runner.py
+│
+├── data_preprocessing.py
+├── train.py
+├── predict.py
+├── evaluate.py
+└── README.md
+
+---
+
+## 🚀 My Contributions
+
+I independently designed and implemented the following components:
+
+### 1. Data Preprocessing Pipeline
+- Handled missing values in medical features:
+	- `Glucose`, `BloodPressure`, `SkinThickness`, `Insulin`, `BMI`
+- Replaced invalid zeros with `NaN`
+- Applied **mean / median imputation**
+- Standardized features using `StandardScaler`
+- Ensured consistent feature names across training and inference
+
+📄 `data_preprocessing.py`
+
+---
+
+### 2. Model Training
+- Implemented a reproducible training pipeline
+- Trained and persisted:
+	- Random Forest classifier
+	- Feature scaler
+- Stored trained artifacts for reuse and deployment
+
+📄 `train.py`
+
+---
+
+### 3. Model Evaluation
+- Added evaluation logic with:
+	- Accuracy
+	- Precision, Recall, F1-score
+- Verified generalization on the test set
+
+📄 `evaluate.py`
+
+---
+
+### 4. Experimentation Framework
+- Benchmarked multiple ML models:
+	- Logistic Regression
+	- Decision Tree
+	- Random Forest
+	- Support Vector Machine (SVM)
+- Automatically reports accuracy and F1-score
+
+📄 `experiments/experiment_runner.py`
+
+#### Sample Results
+
+| Model                 | Accuracy | F1 Score |
+|----------------------|----------|----------|
+| Logistic Regression  | 0.7875   | 0.6320   |
+| Decision Tree        | 0.9875   | 0.9805   |
+| Random Forest        | 0.9950   | 0.9921   |
+| SVM                  | 0.8450   | 0.7328   |
+
+✔️ **Random Forest performs best on this dataset**
+
+---
+
+### 5. Command-Line Prediction Interface
+- Built a CLI-based inference script
+- Ensures:
+	- Correct feature order
+	- Feature-name alignment with trained scaler
+- Predicts diabetes for a single patient input
+
+📄 `predict.py`
+
+Example:
+```bash
+python predict.py \
+	--pregnancies 2 \
+	--glucose 120 \
+	--bp 70 \
+	--skin 20 \
+	--insulin 80 \
+	--bmi 25 \
+	--dpf 0.5 \
+	--age 35
+
+
+
+---
+
+## 🛠️ Tech Stack
+
+- Python 3.10+
+- pandas
+- numpy
+- scikit-learn
+- joblib
+
+---
+
+## 🧩 Notes
+
+- Project is modular and deployment-ready
+- Structured to support FastAPI / Flask integration
+- Generated files cleaned using `.gitignore`
+- Suitable for internship-level ML engineering evaluation
+
+---
+
+## 👩‍💻 Author Contribution
+
+**Contributor:** Tandrita Mukherjee  
+
+**Contribution Scope:**
+- ML pipeline design
+- Data preprocessing
+- Model training & evaluation
+- Experimentation framework
+- CLI-based inference system
+
+---
+
+## 📌 Disclaimer
+
+This repository is a fork of an existing project.  
+All enhancements, restructuring, and ML pipeline components listed above were implemented independently as part of my learning and internship preparation.