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This is collection of My Data Analysis and Python Leaning

Jupyter Notebook 99.91% Python 0.09%
machine-learning machine-learning-algorithms machine-learning-library linear-regression linear-models logistic-regression logistics python pandas pandas-library numpy sklearn titanic-kaggle python-dictionary python-arithmetic basic-python-syntax terrorist-attacks python-list python-string-maupulation scikit-learn

mlportfolio's Introduction

This is collection of My Data Analysis and Python learning

In this, I am trying to accumulate my python knowledge in the form of Tutorial for helping my self and other if they needed.

The wreck of the RMS Titanic was one of the worst shipwrecks in history, and is certainly the most well-known. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and lead to better safety regulations for ships.

I applied the tools of machine learning to predict which passengers survived the tragedy.

The World Happiness Report is a landmark survey of the state of global happiness. The first report was published in 2012, the second in 2013, the third in 2015, and the fourth in the 2016 Update. The World Happiness 2017, which ranks 155 countries by their happiness levels, was released at the United Nations at an event celebrating International Day of Happiness on March 20th.

I did Exploratory Data Analysis (EDA) and applied ML to predict Happiness Score on report 2015

Information on more than 170,000 Terrorist Attacks.The Global Terrorism Database (GTD) is an open-source database including information on terrorist attacks around the world from 1970 through 2016 (with annual updates planned for the future). The GTD includes systematic data on domestic as well as international terrorist incidents that have occurred during this time period and now includes more than 170,000 cases.

Using this Dataset I try to do EDA checked the trending feature of terrorist attacks

In given ipl data set, I did EDA and bring out some interesting statics related to player, team, ground etc.. I tried to find out which ground is best for wining or which player performed well while playing different teams and against other teams. Take a look and enjoy it

The data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be (or not) subscribed.

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