Category:
Machine Learning
Difficulty:
Beginner
Prerequisite(s):
Skills to be Learned:
Fundamentals of simple linear regression, Data handling, Data visualization
Student Marks Prediction Using Linear Regression
Dive into the fascinating world of data science and predictive modeling with 'Student Marks Prediction Using Linear Regression'. This beginner-friendly course offers a comprehensive journey through the basics of linear regression, teaching you how to use Python to predict student performance based on study hours.
This project-based course is designed for beginners looking to explore the world of data science and machine learning. In this course, you will learn to predict student marks based on the number of hours they study. Using the power of simple linear regression and Python programming, you will uncover the relationships between study hours and academic performance. By the end of this course, you'll have the skills and knowledge to perform your own data analysis and make predictions in various domains.
Learning Outcomes:
Upon completing this course, students will be able to:
Understand the fundamentals of simple linear regression and its application in predictive modeling.
Import, preprocess, and visualize data using Pandas, NumPy, Seaborn, and Matplotlib.
Split datasets into training and testing sets for model evaluation.
Build and train a linear regression model to predict student marks.
Evaluate model performance using metrics like mean absolute error.
Apply data visualization techniques to understand the relationship between study hours and scores.
Make accurate predictions for student marks based on study hours.
Prerequisites:
No prior knowledge of data science or machine learning is required. This course is designed for beginners with a basic understanding of Python programming.
Libraries and Programming Language Used:
- Python
- Pandas
- NumPy
- Seaborn
- Matplotlib
- Scikit-Learn
Course Syllabus:
Lecture 1: Defining Objective - What We Aim to Accomplish
- Understanding the Problem Statement
- Defining the Objective of the Course
- Identifying the Predictive Modeling Task
Lecture 2: Setting the Stage - Importing Essential Libraries
- Introduction to Python for Data Analysis
- Importing Pandas, NumPy, Seaborn, and Matplotlib
- Overview of Key Python Libraries
Lecture 3: Loading Data - Your Gateway to Analysis
- Data Acquisition and Sources
- Importing Data into Python
- Exploring the Dataset: Initial Examination
Lecture 4: Unearthing Insights with Exploratory Data Analysis
- Data Exploration Techniques
- Data Cleaning and Handling Missing Values
- Data Visualization with Seaborn and Matplotlib
- Identifying Patterns and Trends in the Data
Lecture 5: Feature Engineering and Model Training
- Feature Selection and Engineering
- Introduction to Linear Regression
- Data Preparation for Model Training
- Building and Training the Linear Regression Model
Lecture 6: Assessing Model Performance
- Model Evaluation Metrics
- Splitting Data into Training and Testing Sets
- Evaluating the Model's Performance
- Interpreting Mean Absolute Error
Lecture 7: Making Predictions - From Input to Output
- Predicting Student Marks with Trained Model
- Applying Predictive Modeling in Real-Life Scenarios
- Understanding Model Predictions and Interpretability
You'll gain hands-on experience with essential libraries like Pandas, NumPy, Seaborn, and Matplotlib, and understand how to process, analyze, and visualize data to uncover meaningful insights. By the end of this course, you'll be equipped to build and evaluate your own linear regression models and apply these skills in various predictive analytics scenarios.