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List of Projects

The document lists five projects focused on machine learning applications. Each project includes an objective, dataset source, and implementation details, covering topics such as sales prediction, email spam classification, diabetes detection, house price prediction, and handwritten digit recognition. The projects utilize various algorithms including linear regression, logistic regression, decision trees, and neural networks.

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29 views1 page

List of Projects

The document lists five projects focused on machine learning applications. Each project includes an objective, dataset source, and implementation details, covering topics such as sales prediction, email spam classification, diabetes detection, house price prediction, and handwritten digit recognition. The projects utilize various algorithms including linear regression, logistic regression, decision trees, and neural networks.

Uploaded by

Trang Hoàng
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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Download as PDF, TXT or read online on Scribd
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List of projects

1. Sales Prediction Using Linear Regression


• Objective: Build a linear regression model to predict sales based on factors like
advertising costs, product stock, and the number of potential customers.
• Dataset: Use available sales datasets from Kaggle or other open e-commerce datasets.
• Implementation: Focus on basic data analysis, cleaning, and building a linear regression
model for prediction.

2. Email Spam Classification Using Logistic Regression


• Objective: Use a logistic regression model to classify emails into two categories: spam
and not spam.
• Dataset: Use a spam email dataset, such as the Spam Email Dataset from the UCI
Machine Learning Repository.
• Implementation: Students work with simple text data processing and apply logistic
regression for classification.

3. Diabetes Detection Using Decision Trees


• Objective: Use a decision tree to detect whether a patient is likely to have diabetes based
on basic health indicators.
• Dataset: Use the Pima Indians Diabetes dataset from UCI or Kaggle, which includes
health metrics such as age, weight, and blood pressure.
• Implementation: Build a basic decision tree model for classification to predict the
likelihood of diabetes.

4. House Price Prediction Using Multiple Linear Regression


• Objective: Predict house prices based on factors like area, number of bedrooms, and
location.
• Dataset: Use a housing dataset such as the Boston Housing Dataset available in Scikit-
learn or on Kaggle.
• Implementation: Focus on building a multiple linear regression model and evaluating its
performance.

5. Handwritten Digit Recognition Using a Simple Neural Network


• Objective: Use a simple neural network to classify handwritten digits.
• Dataset: Use the well-known MNIST dataset, which contains images of handwritten
digits.
• Implementation: Students build a simple neural network with 1-2 hidden layers to
classify digits from images.

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