nitinjosephrepo/Visualization_with_matplotlib

As far as possible I have made effort to use just Matplotlib and used Numpy for charts where the pure matplotlib solution could be very verbose.

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nitinjosephrepo/Titanic_dataset_with_seaborn

As part of my Data science journey I use Seaborn to visualize information from Titanic Dataset using Seaborn 0.11.1

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nitinjosephrepo/Time-Series-Analysis

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nitinjosephrepo/scrapy-crawl-spider

Project using Scrapy spider with Rules that crawls Website/Pages of Interest and extracts Product info, pagination is dealt by configuring Link Extractor method

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nitinjosephrepo/Predicting-Defaulters-Using-Artificial-Neural-Network

ANN or Deep Learning can be utilized for many areas of marketing. Using Neural network models by BrainMaker, Microsoft increased its direct mail response rate my 4.9% to 8.2%. This helped Microsoft bring same amount of revenue for 35% les cost.

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nitinjosephrepo/Predicting-Customer-Lifetime-Value

In Marketing, the CLV is one of the key metrics to have and monitor. The CLV measures customer's total worth to the business over the course of their lifetime relationship to a business. This metric is especially important to keep track of for acquiring new customers.

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nitinjosephrepo/Predicting-Customer-Churn-and-Factors-Responsible

Predicting which factors are responsible for Telco Customer Churn

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nitinjosephrepo/Practical-Time-Series-Analysis Fork of PacktPublishing/Practical-Time-Series-Analysis

Practical Time-Series Analysis, published by Packt

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nitinjosephrepo/nitinjosephrepo

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nitinjosephrepo/Mining-Insights-From-Customer-Reviews

Use GSDMM Package for Topic Modeling on Yelp Review Corpora, GSDMM works well with short sentences found in reviews.

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nitinjosephrepo/Natural-Language-Processing-Projects

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nitinjosephrepo/Multiple-Linear-Regression-to-Predict-Price-of-Used-Cars

Along with predicting the price of used car we utilize Recursive Feature Elimination from Statsmodel to identify features that have most impact on resale price

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nitinjosephrepo/Multilabel-Segementation-Using-Xgboost

For this Multi-class classification dataset we will be using Xgboost, multi:softprob objective which is a standard alternative to binary:logistic when the dataset includes multiple classes. It computes the probabilities of classification and chooses the highest one.

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nitinjosephrepo/Measuring-Incrementality-With-GeoLift

Measure the true incremental value of your marketing campaigns with GeoLift

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nitinjosephrepo/loveofdata.github.io

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nitinjosephrepo/Estimating-Customer-Lifetime-Value-BG-NBD-Model

BG|NBD Model uses binomial probability to determine Customer Life Time Value and the likelihood of which customers are 'alive'

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nitinjosephrepo/Logistic-Regression

Repo contains my personal Machine Learning projects with emphasis on explanability and Insights that are relevant for various stake holders in a business.

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nitinjosephrepo/Items-Frequently-Bought-Together

To answer which items are frequently bought together we will be using Apriori & FPgrowth Algorithm

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nitinjosephrepo/Data-Prepration-For-ML

Most machine learning algorithms require data to be formatted in a very specific way, so datasets generally require preparation before they can yield useful insights. This repository is to document my study notes as I work through steps that I have personally found most challenging.

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nitinjosephrepo/Dyad

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nitinjosephrepo/Data-Driven-Customer-Segementation

For any marketing campaign, it's critical to understand different behaviours, types and interests of Customers. Especially in targeted marketing, understanding and categorizing customers is an essential step for effective marketing strategies.

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nitinjosephrepo/Conjoint-Analysis-to-uncover-consumer-preferences

Conjoint analysis is a data informed approach to understanding what consumers prefer about a product

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nitinjosephrepo/Changing-Trends-in-Online-Petfood

Use Pytrend which is an Unofficial API for Google Trends to visualize global changes in online purchasing preference in last 3 months

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nitinjosephrepo/Building-Ecommerce-RFM-Model-With-Segementation

The “RFM” in RFM analysis stands for recency, frequency and monetary value. RFM analysis is a way to use data based on existing customer behavior to predict how a new customer is likely to act in the future.

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nitinjosephrepo/Analyzing-Online-Shopper-Purchase-Intention

Analyze online shoppers' purchase intentions using Logistic Regression, K-means clustering & A/B Testing

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nitinjosephrepo/About-Me

Config files for my GitHub profile.

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nitinjosephrepo/Abstractive-Summarization-With-GoogleT5

Repo contains my efforts to use both extractive and abstractive summarization techniques that can help assist with SEO task like Meta Description generation

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nitinjosephrepo/Google-Causal-Impact-Inferring-the-effect-of-an-event

The Causal Impact model lets you examine ecommerce and marketing time series data to understand whether changes have led to a statistically significant performance improvement. Here's how to use PyCausalImpact to analyse changes in marketing activity or in this case on Boeing stock price

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nitinjosephrepo/Using-NLP-Models-To-Improve-SEO

This Repo contains my projects that I have used to improve SEO and Content Strategy

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nitinjosephrepo/Marketing-Mix-Modeling-MMM-Using-Robyn

A New GeneratUsing Robyn aims to reduce human bias in the modeling process, esp. by automating modelers decisions like adstocking, saturation, trend & seasonality as well as model validation. Moreover, the budget allocator & calibration enable actionability and causality of the results

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nitinjosephrepo/Improving-Conversion-Rate-With-Decision-Trees

Use Decison Tree on Bank Marketing Dataset to Identify Customer attributes that are drivers of Conversion. We will then interpret these trained decison tree models by visualizing them using the graphviz package in python.

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nitinjosephrepo/Feature-Selection-Exploratory-Data-Analysis

Effort here is to identify important features in relation to our target variable using multiple Correlation methods based on data type. Feature selection is important for ML models to avoid 'curse of dimensionality' but for this dataset we will be using it build our intution that benefits our later EDA effort

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nitinjosephrepo/Using-Prophet-to-Forecast-Bikeshare-Demand

Prophet time series forecasting model was developed by Facebook and is a powerful tool for predicting future events. Here's how to use it to forecast & understand your Data

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