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Greetings Everyone, This blog represents my working experience during my internship with InfoPillar Solution Pvt. Ltd. in the domain of Machine Learning. In this internship, there were 4 tasks assigned with varying difficulty levels. I am happy to inform you that I successfully completed the given 4 tasks assigned during the internship. Task 1 was related to the Loan Prediction using Machine Learning. The idea behind this ML project is to build a model that will classify how much loan the user can take. It is based on the user’s marital status, education, number of dependents, and employments. You can build a linear model for this project. Task 2 was also related to the Fake News Detection Project. Fake news spreads like a wildfire and this is a big issue in this era. You can learn how to distinguish fake news from a real one. You can use supervised learning to implement a model like this.  Task 3 was Iris Flowers Classification Project. The iris flowers have different species...

Greetings Everyone, This blog represents my working experience during my internship with Letsgrowmore in the domain of Data Science. In the internship, there were 3 tasks given with varying difficulty levels: 1. Beginner  2. Intermediate 3. Advanced. I am happy to inform you that I successfully completed 4 tasks assigned during the internship. Task 1 was related to the flower class identification of the Iris dataset. In that multiple parameters of different flowers were given and we have to identify the correct flower class. Task 2 was also related to the Iris dataset classification but this time, the classifier was changed to Decision Tree Algorithm, and also have to visualize the decision tree. The rest of the task can be visualized in my GitHub repo, here is the link to my GitHub. I am really thankful to Mr. Aman Kesarwani and LetsGrowMore , and their Virtual Internship Program ( VIP ) for providing me the opportunity and enhancing my knowledge in the domain and exploring more ...

  Recognizing Handwritten Digits with Scikit-Learn Scikit-learn library: The scikit-learn library ( http://scikit-learn.org/ ) enables us to approach this type of data analysis in a way that differs slightly from what we’ve used in the previous project. I closely related the data to be analyzed to numerical values or strings, but can also involve images and sounds. Aim: The primary aim of this project involves predicting a numeric value, and then reading and interpreting an image that uses a handwritten font. We will have an estimator with the task of learning through a fit() function, and once it has reached a degree of predictive capability (a model sufficiently valid), it will produce a prediction with the predict() function. Then we will discuss the training set and validation set created this time from a series of images. The hypothesis to be tested: The Digits data set of the scikit-learn library provides numerous datasets that are useful for testing many problems of dat...

  Performing Analysis of Meteorological Data Objective: The main objective is to perform data cleaning, perform analysis for testing the Influences of Global Warming on temperature and humidity, and finally put forth a conclusion. Given Hypothesis: The Null Hypothesis H0 is  “Has the Apparent temperature and humidity compared monthly across 10 years of the data indicate an increase due to Global warming” The H0 means we need to find whether the average Apparent temperature for the month of a month says April starting from 2006 to 2016 and the average humidity for the same period has increased or not. Dataset: The dataset can be obtained from Kaggle. The dataset has hourly temperature recorded for the last 10 years starting from 2006–04–01 00:00:00.000 +0200 to 2016–09–09 23:00:00.000 +0200. It corresponds to Finland, a country in Northern Europe. Source URL:  https://www.kaggle.com/muthuj7/weather-dataset Function for plotting Humidity & Apparent Temperature for each ...