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tanu-khanuja/README.md
github contribution grid snake animation

Data Science Enthusiast | Doctorate in Computational Biomechanics

Hi, I’m Tanu! I absolutely love transforming data into fascinating stories. I started out in biomechanics, using math to tackle brain injury problems.

Now, I freelance and get to work on all kinds of projects with amazing people.

Check out some of the cool projects I’ve been working on, and let’s see how we can make data tell incredible stories together!

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  1. Data-science-from-scratch Data-science-from-scratch Public

    In these notebooks, I show how to build machine learning models using just basic Python, without using any extra libraries. By creating these models from scratch, the goal is to understand how they…

    Jupyter Notebook 2

  2. data_science_essential_packages data_science_essential_packages Public

    This project is a comprehensive guide to essential Python libraries used in data science i.e. NumPy, Pandas, and Scikit-learn. It is structured as a series of Jupyter Notebooks, covering fundamenta…

    Jupyter Notebook

  3. sales-time-series-prediction sales-time-series-prediction Public

    A deep learning-based time series forecasting project using LSTMs to predict future sales with univariate and multivariate analysis, uncovering insights into feature importance and predictive perfo…

    Jupyter Notebook

  4. tweets-sentiment-analysis-using-lstm tweets-sentiment-analysis-using-lstm Public

    This project performs sentiment analysis on tweets from the Sentiment140 dataset. The goal is to classify tweets into positive and negative sentiments using an LSTM model implemented in PyTorch.

    Jupyter Notebook

  5. customer_segmentation customer_segmentation Public

    This project involves a comprehensive Customer Segmentation Analysis for an Australian bike company. The analysis uses RFM model to categorize customer and visualise key metrics, aiming to enhance …

    HTML 2

  6. customer_churn_prediction customer_churn_prediction Public

    This project focuses on customer churn prediction using machine learning techniques, specifically Random Forest (RF) and Decision Tree (DT) models.

    Jupyter Notebook