https://github.com/abdelrhman95/4-essential-python-projects-for-beginners

This repo contains simple projects for data scientist and data analytics
https://github.com/abdelrhman95/4-essential-python-projects-for-beginners

data-science dataanalytics eda python time-series xgboost

Last synced: about 1 month ago
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This repo contains simple projects for data scientist and data analytics

• Host: GitHub
• URL: https://github.com/abdelrhman95/4-essential-python-projects-for-beginners
• Owner: abdelrhman95
• Created: 2022-02-22T23:30:03.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2022-02-27T16:35:00.000Z (almost 3 years ago)
• Last Synced: 2023-07-30T06:23:55.770Z (over 1 year ago)
• Topics: data-science, dataanalytics, eda, python, time-series, xgboost
• Language: Jupyter Notebook
• Homepage:
• Size: 260 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# 4-Essential-Python-Projects-for-Beginners

# The first project you will learn the following (Uber Trips Analysis):

1- Read a dataset and display records from it

2- Use your detective’s hat and uncover hidden patterns in data

3- Find the relationship between different variables

4- Draw insights by visualizing these relationships

# The Second Project (The Discovery of Handwashing):

1- Read a dataset and display records from it

2- Display the relationship between variables through different periods

3- Perform some calculations to create new variables

4- Visualize the effect of handwashing on decreasing the number of deaths

# The Third Project (Predicting Parkinson’s Disease with XGBoost): 

1- Read and explore data

2- Understand the relationship between different variables through visualization

3- Carry out *feature selection to determine the variables that are most related to the target output.

4- Build a machine learning model

5- Use metrics, such as accuracy and ROC curve, to evaluate the model’s performance.

6- Save the trained model into a file to be used for future predictions.

# The Fourth project (Detecting Fake News) :

1- Read and explore a textual dataset

2- Build a machine learning model with TfidfVectorizer

3- Create a confusion matrix and understand its components

4- Evaluate the model’s accuracy