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Name: Nitin Joseph
Type: User
Bio: A Digital Strategist. On Data science journey that meanders between art & science. Leveraging ML for Analytics, Marketing & Human Behaviour
Location: Toronto
Name: Nitin Joseph
Type: User
Bio: A Digital Strategist. On Data science journey that meanders between art & science. Leveraging ML for Analytics, Marketing & Human Behaviour
Location: Toronto
Config files for my GitHub profile.
Repo contains my efforts to use both extractive and abstractive summarization techniques that can help assist with SEO task like Meta Description generation
Analyze online shoppers' purchase intentions using Logistic Regression, K-means clustering & A/B Testing
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.
Use Pytrend which is an Unofficial API for Google Trends to visualize global changes in online purchasing preference in last 3 months
Conjoint analysis is a data informed approach to understanding what consumers prefer about a product
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.
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.
BG|NBD Model uses binomial probability to determine Customer Life Time Value and the likelihood of which customers are 'alive'
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
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
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.
To answer which items are frequently bought together we will be using Apriori & FPgrowth Algorithm
Repo contains my personal Machine Learning projects with emphasis on explanability and Insights that are relevant for various stake holders in a business.
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
Measure the true incremental value of your marketing campaigns with GeoLift
Use GSDMM Package for Topic Modeling on Yelp Review Corpora, GSDMM works well with short sentences found in reviews.
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.
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
Practical Time-Series Analysis, published by Packt
Predicting which factors are responsible for Telco Customer Churn
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.
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.
Project using Scrapy spider with Rules that crawls Website/Pages of Interest and extracts Product info, pagination is dealt by configuring Link Extractor method
As part of my Data science journey I use Seaborn to visualize information from Titanic Dataset using Seaborn 0.11.1
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.