ABOUT

DATA ANALYTICS TEAM
We are a collaborative team engaged in a practical data analytics project. Through this project, we aim to deepen our skills in data analytics by gaining hands-on experience in real-world tasks. Our focus is on: collecting, cleaning, and preprocessing data, conducting basic data analysis and creating visualizations, developing project management skills through teamwork and practical implementation. We gather information from various reliable sources to support our work. This project provides us with an excellent opportunity to apply theoretical knowledge in practice and strengthen our competence in data-driven decision-making.

SOLUTION

In this project, we used the following tools

Tool/Library	Description	Homepage
	NumPy: A library for numerical computations in Python, supporting arrays and matrices.	NumPy Homepage
	Python: The programming language used for this project.	Python Homepage
	Seaborn: A Python visualization library based on Matplotlib.	Seaborn Homepage
	Scikit-learn: A machine learning library for Python.	Scikit-learn Homepage
	Pandas: A data manipulation and analysis library for Python.	Pandas Homepage
	Jupyter Notebook: An interactive environment for running Python code.	Jupyter Homepage
	SimFin: A platform for accessing financial data and analytics.	SimFin Homepage

EVENTS

GATE 1: Plan+Offer

Project developments:

Defining the objective
Data collection
Data storage and initial processing
Cleaning and structuring
Visualization
Data analysis
Documentation and reporting
Presentation of results

GATE 2: Demo

Cleaning and structuring:

Handling missing values, correcting data types,
and organizing the dataset into a structured format
using a pandas DataFrame to prepare it for further analysis.

Visualization:

Bar chart: distribution of market value by sector
Histogram: changes in stock prices
QQ plot: assessment of distribution normality
Tables: financial ratios and market values
Interactive dashboard.

DataAnalysis:

included linear regression, with corresponding
calculations and graphical representations.

GATE 3: Demo

DataAnalysis (Machine learning models):

XGBoost tree model
LSTM model