Machine Learning First Project Session

First Project

Nov 29, 2023, 5:30 – 8:00 PM

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About this event

Sure, here is a description for a machine learning GDSC project (linear regression):

Machine Learning GDSC Project: Predicting House Prices Using Linear Regression

Embark on a journey into the realm of machine learning and explore the powerful technique of linear regression by developing a predictive model for house prices. In this project, you will utilize the linear regression algorithm to forecast the selling price of houses based on various factors such as square footage, number of bedrooms, and location.

Project Overview:

As a part of this project, you will:

Data Collection and Preparation: Gather a dataset of house prices and associated features from reliable sources. Clean and preprocess the data to ensure its quality and consistency.

Exploratory Data Analysis (EDA): Analyze the data visually and statistically to understand the relationships between variables and identify potential patterns.

Feature Engineering: Transform and combine features to create new features that enhance the predictive power of the model.

Model Training and Evaluation: Split the data into training and testing sets. Train a linear regression model using the training data and evaluate its performance on the testing data.

Model Tuning: Optimize the hyperparameters of the linear regression model to improve its predictive accuracy.

Interpretation and Visualization: Analyze the coefficients of the linear regression model to understand the relative importance of different factors in predicting house prices. Visualize the relationship between house prices and key features.

Deployment and Application: Deploy the trained model to make predictions for new houses. Explore the potential applications of the model in real-world scenarios.

Required Skills:

Basic programming skills in Python

Familiarity with data analysis and manipulation libraries such as NumPy and pandas

Understanding of linear regression concepts and implementation

Learning Objectives:

Gain hands-on experience with the linear regression algorithm

Develop practical skills in data preprocessing, feature engineering, and model evaluation

Understand the interpretation of linear regression coefficients

Apply machine learning techniques to solve a real-world problem

Project Deliverables:

A comprehensive project report documenting the data analysis, model development, evaluation, and interpretation

A trained linear regression model capable of predicting house prices

A presentation showcasing your project findings and insights

Target Audience:

This project is suitable for beginners and intermediate learners interested in applying machine learning techniques to real-world problems. Participants should have a basic understanding of Python programming and data analysis concepts

Speaker

  • Ali Elsharkawi

    Google DSC

    Core Team Member

Organizers

  • Abdelrahman Soliman

    GDSC Lead

  • Ahmed Khairallah

    Web Development Head

  • Mohamed Gharib

    GDSC AASTMT-Cairo

    Head of Logistics

  • Jana Muhamad

    AAST

    Core Team Director

  • Ali ElSharkawi

    Machine Learning & AI head

  • Yahya Ayman

    Cyber Security Head

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