# Telecom Churn Modeling

Last Updated on May 3, 2021

Built an Telecom Churn Model using Logistic Regression in Python.

Objective : Predictive modeling which analyzes whether a particular customers will Churn or not based on the Demographics, expenses and service Availed provided.

Model predicts with 80% accuracy.

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# House Price Predictor

Last Updated on May 3, 2021

Here we have taken the information of a valid housing data set consisting of information of 500+ Houses. By taking all attributes as factors we will predict the price of the house. We are going to take advantage of all of the feature variables available to use and use it to analyze and predict house prices. Here we have to predict the price of the house on the basis of the following attributes:

~lot size – Square feet of the house I need. (Numerical)

~Bedroom- How many bedrooms I need? (Numerical)

~bathroom – How many bathrooms I need? (Numerical)

~stories-How many stories building I need? (Numerical)

~driveway –Whether I need a driveway or not? (Binary)1 for yes and 0 for no.

~recreational room-Whether I need a rec room or not? (Binary)1 for yes and 0 for no.

~Gas hot water - Whether I need Gas Hot water or not? (Binary)1 for yes and 0 for no.

~full base- Whether I need a full base or not? (Binary)1 for yes and 0 for no.

~Air condition- Whether I need Air condition or not? (Binary)1 for yes and 0 for no.

By entering all these inputs of the attributes, and by using multivariate regression we will predict the house at price in \$.

We have split the dataset into two parts training and testing set. Then by training the dataset we will use multivariate regression and predict the house of the price in the testing data set.

Here we have also compared actual and predicted price using Machine Learning

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# Nyc Yellow Taxi Prediction

Last Updated on May 3, 2021

I did this project in my second semester of Mtech studies at Ahmedabad University. In NYC, taxicabs come in two varieties: yellow and green; they are widely recognizable symbols of the city. Taxis painted yellow (medallion taxis) are able to pick up passengers anywhere in the five boroughs. in Upper Manhattan, the Bronx, Brooklyn, Queens, Staten Island. The yellow taxi cab was first introduced in 1915 by a car salesman named John Hertz. Hertz decided to paint his taxis yellow because of a study by a Chicago university to establish what color would grab the attention of passers-by more easily. The results proved that yellow with a touch of red was most noticeable. As a result, Hertz started to paint all his taxicabs yellow and went on to start the Chicago-based Yellow Cab Company in 1915. During pre-processing of data there were many outliers such as there was 100 dollars fare for a 0-mile trip. Then there were few outliers in rate code id. We pre-processed and removed them all and cleaned the data. After cleaning the data we visualized data in which we got different insights people like to travel single in the taxi. Area 236 has the most taxi bookings. Also, we observed that at midnight (1 to 6 am) people don’t like to travel much often. FOr the prediction part, we predicted the fare using different regression methods and for taxi booking, we used k means clustering.

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# Heart Disease Classification Via Svm Kernels

Last Updated on May 3, 2021

The aim of the project is to develop a model where where we can predict the Heart Disease in the person.

This will help us to detect the disease whether the person is having the Heart Disease or not. This model will help many people all over the globe to know about the heart disease and it can save many lives.

In this project, a good dataset was necessary to implement and execute the problem statement. Then a good analysis was required to find out the correlations and various distributions among the features. Using various types of plots and charts to find out the number of people affected with this till now and generate a model where we can achieve a good score. So, the problem statement was so clear that we need to find whether the person has heart disease or not. SVM classifier helps us to distinguish and get the results. There are various types of SVM kernels in today's era, then we gave to thought to do on different SVM kernels rather than one-two kernels. In total we applied 14 different types of kernels. The highest accuracy on testing was 90.163% and the kernel was Generalized Hist Intersection SVM kernel. SVM classifier can be used to predict the heart disease and solve the required problems.

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# Heart Attack Prediction

Last Updated on May 3, 2021

I did this project in the first semester of my MTech studies at Ahmedabad University. This project is all about predicting the heart attack based on different parameters such as cholesterol, bp, exercise, age, sex, chest pain type, slope, etc. The dataset size was 27 kb. It had 13 columns and 303 rows, I got this dataset from Kaggle. First I did data cleaning in which I removed outliers, null values, duplicate values. After that, I did some data visualization to get insight from the data. During the data visualization, some insights I got from the data were people mostly aged above 40 are suffering/ suffered from a heart attack once in their life, heart rate and chest pain are highly correlated with a heart attack, stress and cholesterol are also one of the main factors of a heart attack, we can see that the patient suffering from heart disease have high cholesterol as compared to the patient not suffering from heart disease. In this project, I have used different machine learning algorithms to predict the Heart attack. I used Logistic regression in which I got 85% accuracy, and decision tree I got 72% accuracy. In the end, there is a decision tree that shows the parameters affecting in order of correlation.

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# Foreign Direct Investment (Fdi)

Last Updated on May 3, 2021