CognoRise InfoTech - Machine Learning Internship

This repository contains the projects completed during my Machine Learning internship at CognoRise InfoTech. Each task applies machine learning algorithms to solve real-world problems, from classification tasks to recommendation systems.

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Task Overview:

Task 1: Breast Cancer Classification

• Description: Classify breast cancer tumors as malignant or benign using features extracted from mammograms.
• Algorithms: K-Nearest Neighbors (KNN), Random Forest Classifier, Decision Tree Classifier, Logistic Regression
• Skills: Classification Algorithms, Data Preprocessing

Task 2: House Price Prediction

• Description: Predict house prices based on features such as area, number of bedrooms, and location.
• Algorithms: Linear Regression, Random Forest Regression, Decision Tree Regression, Gradient Boosting Regressor
• Skills: Regression Models, Data Exploration, Feature Engineering

Task 3: Spam Email Detection

• Description: Develop a model to classify emails as spam or not spam using their content.
• Algorithms: Logistic Regression, Random Forest Classifier, AdaBoost Classifier, K-Nearest Neighbors (KNN)
• Skills: Text Classification, Feature Extraction

Task 4: Diabetes Prediction

• Description: Predict whether a person has diabetes based on features like glucose levels, BMI, and other health-related factors.
• Algorithms: Random Forest Classifier, K-Nearest Neighbors (KNN), AdaBoost Classifier, Decision Tree Classifier
• Skills: Classification, Medical Data Analysis

Task 5: Sentiment Analysis on Movie Reviews

• Description: Perform sentiment analysis on movie reviews to determine if the sentiment is positive or negative.
• Algorithms: Naive Bayes Classifier, Random Forest Classifier, K-Nearest Neighbors (KNN), XGBoost, Logistic Regression
• Skills: Natural Language Processing (NLP), Text Classification

Task 6: Movie Recommendation System

• Description: Create a movie recommendation system using collaborative filtering techniques based on user preferences and ratings.
• Skills: Recommendation Systems, Collaborative Filtering

Task 7: Digit Recognizer

• Description: Use the MNIST dataset to build a digit recognition model, which classifies handwritten digits (0-9) from 28x28 pixel grayscale images.
• Algorithms: Convolutional Neural Network (CNN)
• Skills: Image Processing, Neural Networks

Task 8: Emoji Prediction

• Description: Create a model that predicts emojis based on text input from social media or text messages.
• Skills: Natural Language Processing, Text-to-Emoji Prediction

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Purpose of Internship:

The purpose of this internship is to apply machine learning algorithms to practical problems, from healthcare and real estate to text classification and recommendation systems. The goal is to enhance the understanding of various ML algorithms and their applications.

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How to Run the Projects:

• Clone this repository and navigate to the folder containing the project you wish to run.
• Install the necessary libraries using pip install -r requirements.txt.
• Load the dataset as provided or download it from the appropriate source (links provided within each task folder).
• Run the script and observe the model training, evaluation, and predictions.

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Contributions and feedback are always welcome. Let’s innovate together!