Student marks prediction model has been added

README.md

@@ -156,112 +156,7 @@ The six major areas of data science include the following:
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 ### Note:

StudentMarksPrediction/Readme.md

@@ -0,0 +1,42 @@
+# Student Marks Prediction with Machine Learning
+
+This project demonstrates a regression model to predict student marks based on the number of courses taken and average daily study time.
+
+## Table of Contents
+
+- [Overview](#overview)
+- [Dataset](#dataset)
+- [Model](#model)
+- [Results](#results)
+- [Contributing](#contributing)
+
+## Overview
+
+The project aims to predict student marks using a simple linear regression model. It uses Python and libraries such as NumPy, pandas, Plotly, and scikit-learn.
+
+## Dataset
+
+The dataset includes three columns:
+- number_courses: Number of courses taken by the student.
+- time_study: Average study time per day.
+- Marks: Marks obtained (target variable).
+
+Place the dataset file Student_Marks.csv in the data/ directory.
+
+## Model
+
+Steps involved:
+1. *Data Preprocessing*: Checking for null values and understanding data distribution.
+2. *Visualization*: Using Plotly to visualize relationships.
+3. *Training*: Splitting data into training and test sets, then applying linear regression.
+4. *Prediction*: Evaluating model performance with test data.
+
+## Results
+
+The model achieves a high accuracy with an R² score of approximately 0.946, indicating a strong correlation between the study time and marks obtained.
+
+## Contributing
+
+Contributions are welcome! Fork the repository, make your changes, and create a pull request.
+
+---

StudentMarksPrediction/Student_Marks.csv

@@ -0,0 +1,101 @@
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