Data Science Internship

This collection of tasks is comprised of the work I completed throughout my internship at the LetsGrowMore Virtual Internship Program.

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Task 1 - Iris Flower Classification

• The dataset comprises numerical attributes, which makes it a suitable choice for those starting to learn about supervised machine learning algorithms and data manipulation.

• Classifies iris flowers into one of three species (Setosa, Versicolor, or Virginica) based on measurements of their sepal and petal length and width

• The dataset consists of 150 instances, with 50 instances for each species.

• Dataset: http://archive.ics.uci.edu/ml/datasets/Iris

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Task 2- Exploratory Data Analysis - Terrorism

• Perform ‘Exploratory Data Analysis’ on dataset ‘Global Terrorism’.

• As a security/defense analyst, try to find out the hot zone of terrorism.

• What all security issues and insights you can derive by EDA?

• You can choose any of the tool of your choice (Python/R/Tableau/PowerBI/Excel/SAP/SAS)

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Task 3 - Prediction using Decision Tree Algorithm

• Create the Decision Tree classifier and visualize it graphically.

• The purpose is to predict the right class accordingly, when fed with any new data to this classifier.

• A visual representation of the Decision Tree will be generated to help better understand the decision-making process of the model

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Task 4 - Stock Market Prediction and Forecasting using Stacked LSTM

• Create a robust model using Recurrent Neural Networks (RNNs), with a particular emphasis on the Long-Short Term Memory (LSTM) architecture.

• The model will be trained on a dataset of historical stock market data to learn from the patterns and relationships in the data. The trained model will then be used to forecast future stock market values.

• By utilizing an LSTM-based RNN model, this project aims to provide accurate predictions that will be useful for making informed investment decisions and managing risks.

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