Skip to content

Latest commit

 

History

History
56 lines (30 loc) · 2.24 KB

README.md

File metadata and controls

56 lines (30 loc) · 2.24 KB
Raw

Data Science Internship

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

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


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)


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


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.