Chapter 2. Project overview || Rahul Inchal

Описание: #dataanalysis #datascience #project

Chapter 1. 1. What is Data Analysis?
   • Chapter 1. What is Data Analysis?  

Problem Statement:
LOANS are the major requirement of the modern world. By this only, Banks get a major part of the total profit. It is beneficial for students to manage their education and living expenses, and for people to buy any kind of luxury like houses, cars, etc.
But when it comes to deciding whether the applicant’s profile is relevant to be granted with loan or not. Banks have to look after many aspects.

So, here we will be using Machine Learning with Python to ease their work and predict whether the candidate’s profile is relevant or not using key features like Marital Status, Education, Applicant Income, Credit History, etc.

Several modules (libraries) of Python provide robust tools for data manipulation, visualisation, and machine learning. Here’s an overview of four key Python modules used for data analysis:
1) Pandas
pandas is an open-source data manipulation and analysis library that provides data structures and functions needed to manipulate structured data seamlessly. It is built on top of NumPy and offers two primary data structures: Series and DataFrame.
Key functionalities of pandas include:
Input/Output: Supports reading and writing data from various file formats like CSV, Excel, and more.
Indexing and Selection: Provides powerful tools for selecting and filtering specific data subsets.
Data Cleaning: Handling missing data, removing duplicates, and handling outliers.
Data Transformation: Merging, joining, and concatenating data; reshaping and pivoting.
Data Aggregation: Grouping, summarising, and computing statistical measures.

2) Matplotlib
Matplotlib is a widely-used Python library for creating static, animated, and interactive visualisations in Python.
It provides a wide variety of charts and plots.
It offers a high degree of control and customisation over the visualisations.

3) Seaborn
Seaborn is built on top of Matplotlib and offers a high-level interface for creating informative and attractive statistical graphics.
It simplifies the process of creating complex visualisations.
It is particularly useful for statistical data visualisation.


4) sklearn (scikit-learn)
sklearn (scikit-learn) is a machine learning library in Python that provides simple and efficient tools for data mining and data analysis. It is built on top of NumPy, SciPy, and matplotlib.
Key Features:
Supervised Learning: Algorithms for classification (predicting categories) and regression (predicting continuous values).
Unsupervised Learning: Algorithms for tasks like clustering (grouping similar data points) and dimensionality reduction (compressing data).
Model Selection and Evaluation: Tools for comparing different models, tuning hyper-parameters, and assessing model performance.
Pipelines: Streamline data preparation, model training, and evaluation.
Wide Range of Algorithms: Implements various algorithms for different tasks, including decision trees, support vector machines, random forests, and more.