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https://github.com/kunalpisolkar24/ml_lab
Collection of practical codes for Savitribai Phule Pune University's Machine Learning Laboratory (410246).
https://github.com/kunalpisolkar24/ml_lab
clustering diabetes-prediction email-spam-classifier k-nearest-neighbours machine-learning sequential-models sppu-computer-engineering
Last synced: about 1 month ago
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Collection of practical codes for Savitribai Phule Pune University's Machine Learning Laboratory (410246).
- Host: GitHub
- URL: https://github.com/kunalpisolkar24/ml_lab
- Owner: kunalPisolkar24
- License: mit
- Created: 2024-09-26T15:31:28.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2024-10-20T16:21:41.000Z (2 months ago)
- Last Synced: 2024-10-20T19:21:22.683Z (2 months ago)
- Topics: clustering, diabetes-prediction, email-spam-classifier, k-nearest-neighbours, machine-learning, sequential-models, sppu-computer-engineering
- Language: Jupyter Notebook
- Homepage:
- Size: 4.75 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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README
## 🤖 Machine Learning Laboratory - SPPU 🤖
[](https://github.com/kunalPisolkar24/ML_Lab/blob/main/LICENSE)
Welcome to the repository for the **Laboratory Practice III (410246)** course, focusing on Machine Learning, part of the Fourth Year Computer Engineering curriculum (2019 Course) at Savitribai Phule Pune University. This repository provides practical implementations and resources to help you explore fundamental machine learning algorithms, data pre-processing techniques, classification, clustering, and neural networks.
🏛️ **Course Information:**
| Feature | Description |
|---|---|
| University | Savitribai Phule Pune University |
| Course Name | Laboratory Practice III (410246) |
| Companion Course | Machine Learning (410242) |
| Credit | 02 |
| Practical Sessions | 04 Hours/Week |
| Examination Scheme | Term Work: 50 Marks
Practical Exam: 50 Marks |
🎯 **Learning Objectives:**
* Understand the importance and applications of machine learning.
* Explore various data pre-processing methods to prepare data for machine learning algorithms.
* Study and implement different classification algorithms.
* Understand the need for and implement multi-class classifiers.
* Learn the working principles of clustering algorithms.
* Gain foundational knowledge of neural network algorithms.
💡 **Course Outcomes:**
Upon successful completion of this laboratory course, students will be able to:
* **CO1:** Identify the needs and challenges of applying machine learning to real-world problems.
* **CO2:** Apply appropriate data pre-processing techniques to improve the efficiency and effectiveness of machine learning algorithms.
* **CO3:** Select and implement suitable supervised machine learning algorithms for real-world applications.
* **CO4:** Implement different types of multi-class classifiers and evaluate their performance.
* **CO5:** Compare and contrast various clustering algorithms based on their strengths and weaknesses.
* **CO6:** Design and implement a basic neural network to solve engineering problems.
📂 **Practical Implementations:**
| Practical No. | Description | Dataset Link |
|---|---|---|
| 1 | **Uber Ride Price Prediction:**
1. Pre-process the Uber fares dataset.
2. Identify outliers.
3. Analyze correlation between features.
4. Implement Linear Regression and Random Forest Regression models.
5. Evaluate model performance using metrics like R-squared, RMSE. | [Uber Fares Dataset](https://www.kaggle.com/datasets/yasserh/uber-fares-dataset) |
| 2 | **Email Spam Classification:**
1. Classify emails as spam or not spam using binary classification.
2. Implement K-Nearest Neighbors and Support Vector Machine algorithms.
3. Analyze and compare the performance of the two classifiers. | [Email Spam Classification Dataset](https://www.kaggle.com/datasets/balaka18/email-spam-classification-dataset-csv) |
| 3 | **Bank Customer Churn Prediction (Neural Network):**
1. Build a neural network to predict customer churn (whether a customer will leave the bank).
2. Pre-process the dataset, split into training and testing sets, and normalize the data.
3. Design, train, and evaluate the neural network model.
4. Analyze the model's accuracy and confusion matrix. | [Bank Customer Churn Modeling](https://www.kaggle.com/barelydedicated/bank-customer-churn-modeling) |
| 4 | **Diabetes Prediction (K-Nearest Neighbors):**
1. Implement the K-Nearest Neighbors algorithm to predict diabetes.
2. Evaluate the model using metrics like confusion matrix, accuracy, error rate, precision, and recall. | [Diabetes Dataset](https://www.kaggle.com/datasets/abdallamahgoub/diabetes) |
| 5 | **Sales Data Clustering:**
1. Implement K-Means clustering or hierarchical clustering on the sales data.
2. Determine the optimal number of clusters using the elbow method. | [Sample Sales Data](https://www.kaggle.com/datasets/kyanyoga/sample-sales-data) |
**Mini Project - Titanic Survivor Prediction:**
Build a machine learning model that predicts which passengers survived the Titanic shipwreck based on passenger data (name, age, gender, socio-economic class, etc.).
Dataset Link: [Titanic Dataset](https://www.kaggle.com/competitions/titanic/data)
🚀 **Getting Started:**
Navigate to the relevant practical implementation directory for instructions, code examples, and dataset details.
🙌 **Contributions:**
Contributions, improvements, and feedback are welcome! If you have any enhancements, bug fixes, or additional examples to share, please open a pull request. Refer to the `CONTRIBUTING.md` file for guidelines.
📄 **License:**
This repository is distributed under the MIT License. You are free to use, modify, and distribute the code for educational and personal projects.