anupriyakush's repositories
Ad-Click-Prediction
Predict whether or not the customers will click on an ad based off their features
Credit-Risk-Prediction-and-Boston-Housing-Price-Prediction
The aim of this project is to predict credit risk defaulters and housing prices in Boston
Sentiment-Analysis-of-Survey-Data
The problem at hand is to analyze the comments using text mining technique by building a sentiment analysis model in R.
E-commerce-Conversion-Funnel-Analysis
Aim of the Assignment The aim of this assignment is to analyze the users and page views dataset from an eCommerce company and provide recommendations to the product team to boost the sales. This project also aims to find out if there are any issues with the dataset used for the assessment.
Data-Wrangling-and-Visualization-Workbook
This project contains different ggplot and dplyr functions and their implementation.
Decision-Trees---Random-Forest-Bagging-Boosting
The goal of this project is to build linear and various tree models and compare model fitness. We have used Boston Housing dataset for this purpose. The response variable of this dataset is medv (Median value of owner-occupied homes) which is continuous quantitative variable. Hence, we will fit a linear model and a regression tree model.
Data-Science-project-using-R
This is a model selection project where I created five models and analysed their p-value, t-value, R squared, BIC and plots. These models include simple linear regression, multivariate model, transformed model, model based on best correlation (two models on this).
Recommender-system-using-Python---pandas
Wiki Definition of a recommender system is - A recommender system or a recommendation system is a subclass of information filtering system that seeks to predict the "rating" or "preference" a user would give to an item. I have used 100k dataset from movielens in this project. I have extensively used dataframes hence the clear choice of library is pandas. This project aims to predict the top five movies users will like based on their preference. I have used item based collaborative filtering because finding the relationship between items is better than finding relationship between people. I have established relationship between movies and based on the correlation, this code will suggest top five movies to consider watching.
Multivariate-Regression-Python
Used multivariate regression to analyze which covariate is the best predictor of the response variable 'Price'
largest-python
A program that repeatedly prompts a user for integer numbers until the user enters 'done'. Once 'done' is entered,largest and smallest of the numbers are printed out. If the user enters anything other than a valid number caught it with a try/except and put out an appropriate message and ignore the number.
email-sender-ith-maximum-no.-of-emails-python
Write a program to read through the mbox-short.txt and figure out who has the sent the greatest number of mail messages. The program looks for 'From ' lines and takes the second word of those lines as the person who sent the mail. The program creates a Python dictionary that maps the sender's mail address to a count of the number of times they appear in the file. After the dictionary is produced, the program reads through the dictionary using a maximum loop to find the most prolific committer.
Append-unique-words-to-list-python-
Open the file romeo.txt and read it line by line. For each line, split the line into a list of words using the split() method. The program should build a list of words. For each word on each line check to see if the word is already in the list and if not append it to the list. When the program completes, sort and print the resulting words in alphabetical order. You can download the sample data at http://www.py4e.com/code3/romeo.txt
Print-all-email-ids-from-sample-data-Python
Open the file mbox-short.txt and read it line by line. When you find a line that starts with 'From ' like the following line: From stephen.marquard@uct.ac.za Sat Jan 5 09:14:16 2008. sample data at http://www.py4e.com/code3/mbox-short.txt