Built on-top of NumPy, SciPy and Matplotlib,the Scikit-learn is the robust library used in machine learning.

Why Scikit Learn ?

• Provides efficient tools for machine learning .
• Provide statistical model including classification, regression, clustering.
• Covers most machine-learning tasks and scales to most data problems.

Installation

Using pip

pip install -U scikit-learn

Using conda

conda install scikit-learn

Features

Scikit-learn library focus more on modeling the data rather than loading, manipulating and summarizing data.Some of the popular groups of models provided by Sklearn are :

1. Supervised Learning algorithms : Linear Regression, Support Vector Machine (SVM), Decision Tree etc.
2. Unsupervised Learning algorithms : clustering, factor analysis,Principal Component Analysis, unsupervised neural networks etc.
3. Cross…

As Discussed in Previous Post,Pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. Let’s get dive into the Basics of Pandas.

If You haven’t Read the Previous post then, click below https://medium.com/@abhishek_iiit/basic-introduction-to-pandas-pandas-series-part-1-ee08073b109

We will be using the Dataframe as shown below, but before that, we try to import Pandas and Numpy by the command:

import NumPy as np
import pandas as pd

Pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. Let’s get dive into the Basics of Pandas.

Case 1: Basic of Pandas Series

According to Wikipedia, NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.

Being the Library of Python, it can perform algorithm Faster than the default Python’s library. Without wasting much time, Lets Dive into the Basics of Numpy that can help in the Data Science field. We will be going through Every Case important in the Numpy

Case 1:Memory Allocation

Harvard University labeled the profession “the sexiest job of the 21st century.” And according to LinkedIn, the career has seen an exponential growth becoming the second-fastest-growing profession. This is the time where anyone can start his/her career in the Data field.

Let’s Know the Basic tools Everyone should know when starting with Data Analysis with Python

Step 1: Getting Started(Data Extraction)

You can use Jupyter Notebook (https://jupyter.org/) directly. It’s a free and open-source web application you can work on.

1.Import your Python Library into your Notebook via