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My_Data_Science_Portfolio

Repository containing portfolio of data science projects completed by me for academic, self learning, and hobby purposes. Presented in the form of jupyter Notebooks, The goal of the projects is to use data science/statistical modelling techniques to find something that is interesting. A typical project consist of finding and cleaning data, analysis, visualization and conclusion. Click on the projects to see full analysis and code.

Projects:

1:- Breast Cancer Analysis ( Decision Tree classification) :- This model gives a prediction about the women who are effected with breast cancer in the breast cancer dataset from "Wisconsin". I made model by using the decision tree classification .This dataset comes from the UCI Machine Learning Repository

2:- Supervised learning (Social Media Network ADs) :- In this model i use K-NN machine learning algorithm on a dataset that tells us whether a customer bought an SUV based on a social network ads or not .

3:-Kernel SVM Project (iris dataset) :- Analyzing the famous "Iris dataset" and classify the species of the flowers by using the SVM(Support vector Machine) algorithm

4:-Linear Regression(Ecommerce Customers) :- In model we analyse whether ecommerce company should focus on its efforts on the mobile app or website development and the effect of the Membership time is really matter or not? for this i use linear regrassion madel.

5:-Random Forest Project-(Loan Prediction) :- In this project we will be exploring publicly available data from https://www.lendingclub.com/info/download-data.action. Lending Club connects people who need money (borrowers) with people who have money (investors). I analyse the dataset and classify the people who are eligble to get loan or not by using the Random Forest classification algorithm

6:-Stock Market Analysis(Apple's stock) :-In this project, we will analyse data from google Finance of the popular tech apple stocks .We will use Pandas to extract and analyze information, visulaize it, analyze risks based of it's performance history.

7:-Titanic Data ( logistic regression) :- I ues the famous Titanic Dataset to predict the people who survived or not by using the Logistic regression algorithm

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