https://github.com/hk151109/prodigy-infotech_ml_tasks
Welcome to the Prodigy Infotech Internship Repository! This repository contains all the tasks and projects I completed as part of my internship at Prodigy Infotech.
https://github.com/hk151109/prodigy-infotech_ml_tasks
Last synced: about 2 months ago
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Welcome to the Prodigy Infotech Internship Repository! This repository contains all the tasks and projects I completed as part of my internship at Prodigy Infotech.
- Host: GitHub
- URL: https://github.com/hk151109/prodigy-infotech_ml_tasks
- Owner: hk151109
- Created: 2024-12-17T08:44:14.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-12-17T09:08:22.000Z (5 months ago)
- Last Synced: 2024-12-17T09:41:39.541Z (5 months ago)
- Language: Jupyter Notebook
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: Readme.md
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README
# Prodigy InfoTech Machine Learning Internship 🚀
This repository contains all the tasks and projects I completed as part of my internship at Prodigy Infotech.
## Overview
During my internship at Prodigy InfoTech, I had the incredible opportunity to work on four diverse and challenging machine learning projects, applying various algorithms and techniques to solve real-world problems.
## Internship Overview 📋
- **Role**: Intern
- **Company**: Prodigy Infotech
- **Duration**: November 2024 - December 2024
- **Focus Areas**: Python Programming, Machine Learning
---
## 🔬 Internship Projects
### Task 1: House Price Prediction 🏠
**Objective:** Implement a linear regression model to predict house prices
- **Techniques Used:** Linear Regression
- **Features:** Square footage, Number of bedrooms/bathrooms
- **Key Deliverables:**
- Predictive model
- Data preprocessing scripts
- Visualization of price trends
### Task 2: Customer Segmentation 🛒
**Objective:** Create a K-means clustering algorithm for retail customer analysis
- **Techniques Used:** K-means Clustering
- **Features:** Customer purchase history
- **Key Deliverables:**
- Customer segments
- Clustering visualization
- Insights report
### Task 3: Image Classification - Cats vs Dogs 🐱🐶
**Objective:** Develop an SVM model to classify images of cats and dogs
- **Techniques Used:** Support Vector Machines (SVM)
- **Dataset:** Binary image classification
- **Key Deliverables:**
- Trained classification model
- Confusion matrix
- Model performance metrics
### Task 5: Food Item Recognition and Calorie Estimation 🍲
**Objective:** Design a model to recognize food items and estimate calorie content
- **Techniques Used:** Deep Learning, Image Recognition
- **Features:** Food image analysis, Calorie estimation
- **Key Deliverables:**
- Food recognition model
- Calorie estimation algorithm
- Detailed analysis report
---
## 📄 Official Documents
#### Offer Letter:
#### Certificate of Completion:
#### Letter of Recommendation:
## 🛠 Technologies & Tools
- Python
- Machine Learning Libraries
- Scikit-learn
- Pandas
- NumPy
- Matplotlib
- TensorFlow/Keras
## 🏆 Key Learnings
- Advanced machine learning techniques
- Data preprocessing
- Model evaluation
- Practical application of ML algorithms
---
## 🔒 License
This project is open-source and available under the MIT License.