My Projects
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Newsletter Form
The signup page features a clean and modern design crafted using Bootstrap's pre-built components and responsive grid system, ensuring a visually appealing layout across different devices.
Python Pomodoro App
The application can be launched by running the Python script. Users can adjust the work and break intervals and start the timer. Visual and auditory cues assist in tracking the intervals.
Mushroom Naive Bayes Classifier
The Mushroom Naive Bayes Classifier project utilizes the Naive Bayes algorithm to classify mushroom species based on features such as shape, color, and odor. The data is loaded into a DataFrame and preprocessed before training the classifier. The model's performance is evaluated using metrics such as accuracy, achieving an accuracy of 93.6%.
Naive Bayes Classifier for Diabetes Prediction
The Naive Bayes Classifier for Diabetes Prediction project demonstrates the implementation of machine learning techniques, particularly the Naive Bayes algorithm, for predicting diabetes risk based on various health parameters. The project utilizes Python and popular libraries such as pandas, scikit-learn, and seaborn for data preprocessing, model training, and visualization.
Naive Bayes Classifier for Spam-Ham Analysis
This project demonstrates the implementation of the Naive Bayes algorithm for classifying emails as spam or ham. It analyzes the content of emails and predicts whether they are spam (unsolicited or unwanted emails) or ham (genuine emails). The project utilizes Python and popular libraries such as pandas and scikit-learn for data preprocessing, model training, and evaluation.
Naive Bayes Classifier for Twitter Sentiment Analysis
This project demonstrates the implementation of the Naive Bayes algorithm for sentiment analysis on Twitter data. It predicts the sentiment (positive, negative, or neutral) of tweets based on their content. The project utilizes Python and popular libraries such as pandas, scikit-learn, and tweepy for data collection, preprocessing, model training, and evaluation.