Sanchit Misra
sanchitmisra
Data Scientist and Business Analyst Data Science | Machine Learning | Python | SQL | Excel | Model Building
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Classification model was created to conduct an analysis that can detect the Non- Human Traffic presence on website using, Gradient Boosting Classifier & RF.
Developed natural language processing (NLP) model to extract sentiments from customer reviews through text pre-processing approach (Tokenization, Stemming, Lemmatization, TF-IDF Vectorizer). Logistic Regression, Gaussian NB, and Gradient Boosting were used to speed up the accuracy to 92%.
The objective of this research is to develop a prediction model using supervised regression machine learning that support the start-up for a stable supply of rental bikes.
Predicting the Click Through Rate (CTR) for an email campaign.
Repositories
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Classification model was created to conduct an analysis that can detect the Non- Human Traffic presence on website using, Gradient Boosting Classifier & RF.
Developed natural language processing (NLP) model to extract sentiments from customer reviews through text pre-processing approach (Tokenization, Stemming, Lemmatization, TF-IDF Vectorizer). Logistic Regression, Gaussian NB, and Gradient Boosting were used to speed up the accuracy to 92%.
The objective of this research is to develop a prediction model using supervised regression machine learning that support the start-up for a stable supply of rental bikes.
Predicting the Click Through Rate (CTR) for an email campaign.
Predicting the median price of homes by Linear Regression model.
The goal of this project is to investigate and analyze data in order to uncover crucial information about terrorist operations around the world. The most afflicted countries, the most notorious groups, and their motives are all found in this dataset, which spans the years 1970 to 2017.
Created a functional vending_machine using conditional statements.
From the given 'Iris' dataset, Predict the optimum number of clusters and represent it visually.
Predicting the percentage of marks of a student based on the number of study hours using Supervised Machine Learning.