big-data-modeling-and-analytics

Understand the fundamentals of big data and MapReduce. Use Spark and PySpark to solve big data problems. Use SQL for NoSQL data (DataFrames in Spark)

causal-effect-of-cigarettes-on-chd

Is cigarette smoked per day a factor in determining whether a patient suffers from Coronary Heart Disease? In this analytical study, we can conclude the consistent evidence supporting a causal role of smoking in causing coronary heart disease. Thus establishing a causal relationship between the two.

data-science-with-python

Use a complete set of open source tools for data science in Python, including the Jupyter Notebook, NumPy, Pandas, Seaborn, scikit-learn, Colab, and many others. Cover the various phases of exploratory data analysis: importing data, cleaning and transforming data, algorithmic thinking, grouping, aggregation, reshaping, visualization, time series, statistical modeling, and data exploration and communication of results.

econometrics

harshtandon23

machine-learning-algorithms

Classifying 'Survived' and 'Not Survived' from Titanic dataset using Machine Learning algorithms such as Gradient Boosting, Random Forest, Multi Layer Perceptron, SVM and Logistic Regression.

machine-learning-facebook-ads-click-prediction

You have been hired as a consultant to a start-up that is running a targetted marketing ads on facebook. The company wants to anaylze customer behaviour by predicting which customer clicks on the advertisement.

machine-learning-in-finance

Supervised, Unsupervised, Reinforcement Learning, PCA and Dimensionality Reduction. We'll also look into Data Visualization and Sequence Modeling. This repo contains my answers for Coursera's Machine Learning in Finance course.

marketing-analytics

Apply Media Mix Modeling using AdStock variables, Segmentation and Targeting, Forecasting using Bass Models, Multinomial Logit Model to help assess the results of campaigns and tests, and Customer Acquisition or Churn Predictions Using Hazard/Survival Model.

micro-economics

Use of regression analysis to derive supply and demand estimates from market data. Profit maximization and marginal analysis. Supply and demand equilibrium and effects of disequilibrium. Price, income and cross-price elasticities of demand and supply. Consumer behavior and derivation of individual demand curves. Production and costs, including identification of relevant costs, cost functions, fixed and variable costs. Market supply curve in the short- and long-run under perfect competition. How market structure (perfect competition, monopoly, monopolistic competition or oligopoly) affects optimal firm choices. Pricing strategies for firms with market power.

movie-recommendation-system-using-ml

Menu Driven program to recommend movies to a selected user. Recommendation System is created using Low Rank Matrix Factorization.

neural-machine-translator

Based on Google's paper 'Attention Is All You Need', this sequence to sequence model speeds up RNNs by using 'Attention', enabling parallelization.

r-programming

Manipulate and Visualize data using the R programming language. Data manipulation includes importing/exporting data from any source, cleaning, merging, transforming, and reshaping datasets, generating variables using existing data as well as through simulation, and developing programs to generate custom functions that are not available in existing packages. Data visualization includes generating descriptive statistics, tables, graphs, and maps, as well as improving graphs by manipulating plot parameters.