Sayantan Codes

LetsGrowMore offers internships in various domains- Web development Data Science App development Campus Ambassador Content writing Social Media Marketing Graphic designing Recently I have completed my Data Science internship at LetsGrowMore.  I believe that the experience I gained over the course of this internship will help me in my future endeavors. I picked up a lot of skills along the way in the field of data analytics. During this internship, I completed 2 tasks: 1 beginner level task and 1 advanced level task. Beginner Level: Task 4 - Image to Pencil Sketch with Python Link:  https://lnkd.in/ehF_jb95 Advanced Level: Task 1 - Develop A Neural Network That Can Read Handwriting Link:  https://lnkd.in/ejZp4XVE All in all, I have gained some much-needed experience in solving data science tasks. I was able to experiment with various modules and projects built for data science and expand my horizons. I would like to thank  Aman Kesarwani  and  LetsGrowM...

Recognizing Handwritten Digits by Sayantan Bhattacharyya Language used: Python Libraries used: Matplotlib, Scikit-learn   Overview: Recognizing handwritten text is a problem that can be traced back to the first automatic machines that needed to recognize individual characters in handwritten documents. Think about, for example, the ZIP codes on letters at the post office and the automation needed to recognize these five digits. Perfect recognition of these codes is necessary to sort mail automatically and efficiently. Included among the other applications that may come to mind is OCR (Optical Character Recognition) software. OCR software must read handwritten text, or pages of printed books, for general electronic documents in which each character is well defined. But the problem of handwriting recognition goes farther back in time, more precisely to the early 20th Century (1920s), when Emanuel Goldberg (1881–1970) began his studies regarding this issue and suggested that a statisti...

Meteorological Data Analysis by Sayantan Bhattacharyya Language used: Python Libraries used: Numpy, Pandas, Matplotlib, Seaborn Data source::  Click here Overview: In this project, we are doing hypothesis testing on whether the dataset and trying to prove that hypothesis is correct or not. We are also doing some data cleaning techniques, Data visualization, and hypothesis testing. 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 say April starting from 2006 to 2016 and the average humidity for the same period has increased or not. Monthly analysis has to be done for all 12 months over the 10 year period.  So basically, we have to resample our data from hourly to monthly, then comparing the same month over the 10 year period. Then we support our analysis with appropriate...