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This repository contains all data science projects taken in the IBM data science course.

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House_Sales_in_King_Count_USA.ipynb
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ML0101EN-Clas-Decision-Trees-drug.ipynb
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ProjectForDataScience.ipynb
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DATA SCIENCE PROJECT.

CONTENTS.

1. Python Basics: Data Types, Functions,Objects and Classes.

2. Python Basics-Libraries: Panda, Numpy.

3. File Editing: WRITING FILES WITH WRITE, READING FILES WITH READ,APPENDING FILES,COPY A FILE.

4. Web-Scrapping: REQUEST,BEAUTIFUL SOUP,JSON library.

5. Extracting and Visualizing Stock Data: REQUEST,BEAUTIFUL SOUP,yfinance.

6. Data Wrangling: pandas,numpy,matplotlib.

7. Exploratory Data Analysis: correlation,seaborn.

8. Model Development: Pandas,Numpy, Pipeline,Linear and Nonlinear Regression.

9. Model Evaluation And Refinement: Pandas,Numpy,Scikitlearn, Data Transformation, Data spliting, Cross Validation, Rigde Regression, GridSearch.

10. K-Nearest Neigbour: Pandas,Numpy,Scikitlearn, confusion matrix, Jaccard index, F1-score.

11. Decision Tree: Pandas,Numpy,Scikitlearn, confusion matrix, Jaccard index, F1-score,descision tree classifier.

12. Taxi Tip Prediction: Pandas,Numpy,Scikitlearn, Decsision Tree Regression, logistic regression, Random Forest.

13. House Sales In King County: Pandas,Numpy,Scikitlearn, Data transformation, Linear Regression, Rigde Regression, Grid Search.

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This repository contains all data science projects taken in the IBM data science course.

Topics

python pandas data-wrangling decision-trees k-nearest-neighbours model-evaluation scikitlearn-machine-learning model-development model-refinement exporatory-data-analysis

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