This project involves conducting an exploratory data analysis on the Global Terrorism Database to find out the hot zone of terrorism.
- The dataset used for this project can be found at the following link: https://bit.ly/2TK5Xn5.
- Pandas
- Numpy
- Matplotlib
- Seaborn
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'Exploratory Data Analysis on Dataset - Terrorism.ipynb': This is the Jupyter notebook containing the code for the project.
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'globalterrorismdb_0718dist.csv': This is the dataset used for the project.
- Clone the repository
- Open 'Exploratory Data Analysis on Dataset - Terrorism .ipynb' using Jupyter Notebook or Google Colab
- Run each cell of the notebook
This project involves creating a Decision Tree classifier to predict the class of an object based on its attributes.
- The dataset used for this project can be found at the following link: https://bit.ly/3kXTdox.
- Pandas
- Numpy
- Scikit-Learn
- Matplotlib
- 'Prediction using Decision Tree Algorithm.ipynb': This is the Jupyter notebook containing the code for the project.
- 'Iris.csv': This is the dataset used for the project.
- Clone the repository
- Open 'Prediction using Decision Tree Algorithm.ipynb' using Jupyter Notebook or Google Colab
- Run each cell of the notebook
This project involves predicting and forecasting the stock prices of Tata Global Beverages Limited using a stacked LSTM model.
- The dataset used for this project can be found at the following link: https://raw.githubusercontent.com/mwitiderrick/stockprice/master/NSE-TATAGLOBAL.csv.
- Pandas
- Numpy
- Matplotlib
- Keras
- Scikit-Learn
- 'Stock Market Prediction And Forecasting Using Stacked LSTM.ipynb': This is the Jupyter notebook containing the code for the project.
- 'NSE-TATAGLOBAL.csv': This is the dataset used for the project.
- Clone the repository
- Open 'Stock Market Prediction And Forecasting Using Stacked LSTM.ipynb' using Jupyter Notebook or Google Colab
- Run each cell of the notebook