Repository containing portfolio of Data Science projects completed by me for academic, self learning, and hobby purposes. Presented in the form of iPython Notebooks.
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Supervised Learning - Boston House Price Prediction: A model to predict the value of a given house in the Boston real estate market using various statistical analysis tools. Identified the best price that a client can sell their house utilizing machine learning.
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Unsupervised Learning - Click Through Rate (CTR) Prediction: Developed a model to predict whether a particular internet user will click on the advertisement or not based on a dataset containing data of various customers.
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Deep Learning - Bank Churn Prediction: Designed and implemented an Artificial Neural Network that learns to predict customer churn using binary classification.
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Deep Learning - CNN Image Classifier(Cats vs Dogs): Designed and implemented a Convolutional Neural Network that learns to recognize images of cats and dogs and based on that learning predicts that a given image is of cat or dog.
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Tools: Scikit-learn, Pandas, Keras, Seaborn, Matplotlib, Featuretools
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- Sentiment Analysis for Review Classification: 2-way polarity (positive & negative) classification system for reviews, using Natural Language Toolkit(NLTK).
Tools: NLTK, Scikit-learn, Pandas and Seaborn
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- Titanic Dataset - Exploratory Analysis: Exploratory Analysis of the passengers onboard RMS Titanic using Pandas and Seaborn visualisations.
Tools: Pandas, Seaborn and Matplotlib
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- ML with K-means Clustering: Using K-menas clustering to classify customer segments into different clusters based on their spending score.