🧑‍🎓 Logistic Regression Student Pass/Fail prediction

This repo contains Logistic Regression project where I take random 1000 students records from different universities This repo will predict chance of a student Pass/Back based on academic & lifestyle factors

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Dataset constains :

Records of 1000 random student records:

• study_hour , sleep_hour
• class_attendence, study_AM/PM
• internet_access, exam_score
• pass/back

Dataset Overview

• Source: https://www.kaggle.com/datasets/kundanbedmutha/exam-score-prediction-dataset (kaggle site)
• Format : 1000 students record on 7 parameters
• Target feature: Pass/Back

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Step performed in Project

• Import Python packages & Datasets
• Exploratory Data Analysis (EDA)
  • Graphs & Charts
  • Distribution of Exam level
  • Sleep hours impact on result
  • Night study vs performence vs internet access
• Features Engineering
• Train the Model
  • Train_Test_Split
  • accuracy
  • confusion metrix
  • classification report & Cross Validation

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Packages Used In :

• python
• pandas
• numpy
• matplotlib
• seaborn
• sklearn

How to access this repo on Devise

• git clone https://github.com/Lakhan-cs/Logistic-Regression.git (On terminal)
• pip install -r requirements.txt (essential packages on bash commond)
• cd "Lakhan-cs/Linear-reg_project" (Access the repo file can also modify the repo.)