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National Stock Exchange- Tata Global Price Close Model Prediction

07.-Pandas

Work All Scenario Of Pandas Library For Data Analysis

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19.-Analyzing-Police-Activity-With-Python-And-Pandas

Now that you have learned the foundations of pandas, this course will give you the chance to apply that knowledge by answering interesting questions about a real dataset! You will explore the Stanford Open Policing Project dataset and analyze the impact of gender on police behavior. During the course, you will gain more practice cleaning messy data, creating visualizations, combining and reshaping datasets, and manipulating time series data. Analyzing Police Activity with pandas will give you valuable experience analyzing a dataset from start to finish, preparing you for your data science career!

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29.-Logistic-Regression

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14.-Seaborn

Data Visualization With python most attractive library seaborn

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20.-Python-For-Data-Analysis-Pandas

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02.-Python-For-Data-Analysis---Numpy

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27.-Data-Capstone-Project

HTML

21.-Panda-Excercises

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24.-Pandas-Built-in-Data---Viz

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04.-Data-Visualization-With-Seaborn

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22.-Data-Visualization-With-Matplotlib

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13.-Data-Science-For-All

Including all the modules of python

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03.-What-Your-Name-Telling-You

In this project, you will explore the baby names dataset compiled by the Social Security Administration. The data (name, year of birth, sex and number) are from a 100 percent sample of Social Security card applications after 1879. This dataset is a minehouse and contains lots of interesting stories to explore. In this project, you will explore some of these interesting stories around trendiness in names, and estimating a person's age from their name.

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26.-Graphical-Plotting

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25.-Plotly-And-Cufflinks

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28.-Linear-Regression

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23.-Data-Visualization-With-Seaborn

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15.-Starting-With-Statistical-Numpy

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08.-Matplotlib

Practices

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01-Python-Crash-Course

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17.-Machine-Learning---Part-02---Regression-Model

In the previous chapter, you made use of image and political datasets to predict binary as well as multiclass outcomes. But what if your problem requires a continuous outcome? Regression, which is the focus of this chapter, is best suited to solving such problems. You will learn about fundamental concepts in regression and apply them to predict the life expectancy in a given country using Gapminder data.

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18.-Merging-DataFrame-With-Pandas

As a Data Scientist, you'll often find that the data you need is not in a single file. It may be spread across a number of text files, spreadsheets, or databases. You want to be able to import the data of interest as a collection of DataFrames and figure out how to combine them to answer your central questions. This course is all about the act of combining, or merging, DataFrames, an essential part of any working Data Scientist's toolbox. You'll hone your pandas skills by learning how to organize, reshape, and aggregate multiple data sets to answer your specific questions.

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05.-Customizing-Numpy-Framework

NumPy is a Python package to efficiently do data science. Learn to work with the NumPy array, a faster and more powerful alternative to the list, and take your first steps in data exploration.

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09.-Introduction-To-Python-For-Data-Science

Python is a general-purpose programming language that is becoming more and more popular for doing data science. Companies worldwide are using Python to harvest insights from their data and get a competitive edge. Unlike any other Python tutorial, this course focuses on Python specifically for data science. In our Intro to Python class, you will learn about powerful ways to store and manipulate data as well as cool data science tools to start your own analyses.

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12.-Machine-Learning---Part-01---Data-Preprocessing

Data Preprocessing is a data mining technique that involves transforming raw data into an understandable format. Real-world data is often incomplete, inconsistent, and/or lacking in certain behaviors or trends, and is likely to contain many errors. Data Preprocessing is a proven method of resolving such issues

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16.-Statistical-Plots-With-Seaborn

This is a high-level tour of the Seaborn plotting library for producing statistical graphics in Python. The tour covers Seaborn tools for computing and visualizing linear regressions as well as tools for visualizing univariate distributions (e.g., strip, swarm, and violin plots) and multivariate distributions (e.g., joint plots, pair plots, and heatmaps). This also includes a discussion of grouping categories in plots.

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11.-Intermediate-Python-For-Data-Science

The intermediate python course is crucial to your data science curriculum. Learn to visualize real data with matplotlib's functions and get to know new data structures such as the dictionary and the Pandas DataFrame. After covering key concepts such as boolean logic, control flow and loops in Python, you're ready to blend together everything you've learned to solve a case study using hacker statistics.

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vivekg0510/06.-Prediction-Model

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