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https://github.com/saptarshi11/prodigy_trackcode_ml

PRODIGY INTERNSHIP - MACHINE LEARNING During my internship at Prodigy Infotech, I tackled diverse machine learning projects: House Price Prediction with linear regression. Customer Segmentation via K-means clustering. Cat vs Dog classification using SVM. Hand Gesture Recognition with CNNs. Food Recognition and calorie estimation from images.
https://github.com/saptarshi11/prodigy_trackcode_ml

cnn cnn-model handgesture-recognition house-price-prediction svm-model

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PRODIGY INTERNSHIP - MACHINE LEARNING During my internship at Prodigy Infotech, I tackled diverse machine learning projects: House Price Prediction with linear regression. Customer Segmentation via K-means clustering. Cat vs Dog classification using SVM. Hand Gesture Recognition with CNNs. Food Recognition and calorie estimation from images.

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# PRODIGY_TrackCode_ML

PRODIGY INTERNSHIP - MACHINE LEARNING



I would like to express my gratitude to Prodigy Infotech for providing me with the opportunity to work on these projects and for the invaluable support and guidance throughout my internship journey. Here is my approach on the tasks provided to me



TASK 1 : House Price Prediction



This project demonstrates how to build a simple linear regression model to predict the prices of houses based on their square footage, number of bedrooms, and number of bathrooms.



TASK 2 : Customer segmentation



This project aims to segment customers of a retail store based on their purchase history. Using K-means clustering, customers are grouped into clusters according to their purchase frequency and total amount spent. The dataset used for this analysis includes synthetic customer purchase history data, containing information such as how often customers make purchases and the total amount they spend. The K-means algorithm is applied to this data to identify distinct customer segments, allowing the retail store to tailor marketing strategies and product offerings to different customer groups effectively. The project includes code for generating the synthetic dataset, performing K-means clustering, and visualizing the resulting clusters.



TASK 3 : Cat vs Dog



This project implements a Support Vector Machine (SVM) classifier to classify images of cats and dogs using the TensorFlow Datasets (TFDS) Cats vs Dogs dataset. The dataset consists of a large set of images of cats and dogs. The images are preprocessed, resized, and normalized before being fed into the SVM model. The SVM model is trained on a subset of the dataset and achieves a certain accuracy level. Finally, the trained model is tested on a sample image to demonstrate its classification capabilities. This project provides a simple yet effective example of using machine learning algorithms, specifically SVM, for image classification tasks.



TASK 4 : Hand Gesture



This project focuses on developing a hand gesture recognition model capable of accurately identifying and classifying various hand gestures from image or video data. The goal is to enable intuitive human-computer interaction and gesture-based control systems. The model will be trained on a dataset containing images or video frames of different hand gestures, such as thumbs up, peace sign, pointing, etc. Advanced deep learning techniques, such as convolutional neural networks (CNNs), may be employed to extract meaningful features from the hand gesture images. The trained model will then be capable of recognizing and classifying unseen hand gestures with high accuracy, facilitating seamless interaction between users and devices through intuitive gestures.



TASK 5 : Food Recognition



This project aims to develop a model that accurately recognizes food items from images and estimates their calorie content. The model's objective is to enable users to track their dietary intake and make informed food choices by providing calorie information for various food items. The model will be trained on a diverse dataset containing images of different food items along with their corresponding calorie values. Deep learning techniques, such as convolutional neural networks (CNNs) may be utilized to recognize food items from images and estimate their calorie content. The trained model will then be capable of accurately identifying a wide range of food items from images and providing users with estimated calorie values, empowering them to maintain a healthy diet and monitor their nutritional intake effectively.