STUDENTS PERFORMANCE PREDICTION USING MACHINE LEARNING ALGO

MLpython

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+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.linear_model import LinearRegression
+
+# Load dataset
+data = pd.read_csv("dataset.csv")
+
+X = data[['hours_study','sleep_hours','attendance']]
+y = data['marks']
+
+# Train model
+model = LinearRegression()
+model.fit(X, y)
+
+print("=== AI Study Companion ===")
+hours = float(input("Enter study hours: "))
+sleep = float(input("Enter sleep hours: "))
+attendance = float(input("Enter attendance %: "))
+
+# Prediction
+prediction = model.predict([[hours, sleep, attendance]])[0]
+
+print(f"\n Predicted Marks: {prediction:.2f}")
+
+# Smart Suggestions
+if prediction < 50:
+    print("You need serious improvement. Increase study hours!")
+elif prediction < 75:
+    print("You're doing okay. Stay consistent!")
+else:
+    print("Excellent! Keep it up!")
+
+# Graph
+plt.scatter(data['hours_study'], data['marks'])
+plt.xlabel("Study Hours")
+plt.ylabel("Marks")
+plt.title("Study Hours vs Marks")
+plt.show()

MLpython.py

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+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.linear_model import LinearRegression
+
+# Load dataset
+data = pd.read_csv("dataset.csv")
+
+X = data[['hours_study','sleep_hours','attendance']]
+y = data['marks']
+
+# Train model
+model = LinearRegression()
+model.fit(X, y)
+
+print("=== AI Study Companion ===")
+hours = float(input("Enter study hours: "))
+sleep = float(input("Enter sleep hours: "))
+attendance = float(input("Enter attendance %: "))
+
+# Prediction
+prediction = model.predict([[hours, sleep, attendance]])[0]
+
+print(f"\n Predicted Marks: {prediction:.2f}")
+
+# Smart Suggestions
+if prediction < 50:
+    print("You need serious improvement. Increase study hours!")
+elif prediction < 75:
+    print("You're doing okay. Stay consistent!")
+else:
+    print("Excellent! Keep it up!")
+
+# Graph
+plt.scatter(data['hours_study'], data['marks'])
+plt.xlabel("Study Hours")
+plt.ylabel("Marks")
+plt.title("Study Hours vs Marks")
+plt.show()

README.md

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+# My Project: Student Performance Predictor
+
+This project predicts student marks using Machine Learning.
+
+## Features
+- Predict marks using:
+  - Study hours
+  - Sleep hours
+  - Attendance
+- Smart suggestions
+- Graph visualization
+
+## Tech Stack
+- Python
+- Pandas
+- Scikit-learn
+- Matplotlib
+
+## Files
+- MLpython.py
+- dataset.csv
+
+
 ## 500 + 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗟𝗶𝘀𝘁 𝘄𝗶𝘁𝗵 𝗰𝗼𝗱𝗲
 
 ***500 AI Machine learning Deep learning Computer vision NLP Projects with code* !!!**

dataset.csv

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+hours_study,sleep_hours,attendance,marks
+2,6,60,50
+3,7,65,55
+4,6,70,60
+5,7,75,65
+6,8,80,70
+7,7,85,75
+8,8,90,85