{"id":18926739,"url":"https://github.com/durgeshsamariya/100daysofdatascience","last_synced_at":"2025-04-15T13:33:19.130Z","repository":{"id":65294815,"uuid":"286945728","full_name":"durgeshsamariya/100DaysofDataScience","owner":"durgeshsamariya","description":"A 100 Day DS Challenge to learn and implement DS concepts ranging from the beginner of Data Science to Data Scientist. 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I will share my progress via :\n\nGitHub: https://github.com/themlphdstudent/100DaysofDataScience\n\nTwitter : https://www.twitter.com/@themlphdstudent\n\nInstagram : https://www.instagram.com/@themlphdstudent\n\nMy Webpage : https://www.durgeshsamariya.com\n\nMedium : https://medium.com/@themlphdstudent\n\nApart from that all notebooks are available on my [kaggle](https://www.kaggle.com/themlphdstudent) or [Kaggle GitHub repo](https://github.com/themlphdstudent/kaggle).\nMake sure you subscribe and give star to the repository.\n\n## Day 1 | 12 August 2020\n\nDay 1 of #100DaysofDataScience\nDataset: Countries Population from 1955 to 2020\n\nPart - 1 | Today's Questions are :\n1. Top 30 countries by Population in 2020.\n2. Total Share in the World's population by the top 10 countries in 2020.\n3. Country Wise Data Analysis.\n\nYou can find kaggle kernel [here](https://www.kaggle.com/themlphdstudent/world-population-from-1955-to-2020).\n\n## Day 2 | 13 August 2020\n\nDay 2 of #100DaysofDataScience\nDataset: Countries Population from 1955 to 2020\n\nPart - 2 | Today's Questions (Part - 2) are :\n1. The population of the top five countries since 1955.\n2. Which country is highly dense in 2020?\n3. Which country has the lowest density by population in 2020?\n\nYou can find kaggle kernel [here](https://www.kaggle.com/themlphdstudent/world-population-from-1955-to-2020).\n\n## Day 3 | 14 August 2020\n\nDay 3 of #100DaysofDataScience\nDataset: Countries Population from 1955 to 2020\n\nPart - 3 | Today's Questions are :\n1. Which Country is Highly Populated? [China]\n2. Number of Countries with Population more than 1 Million. [159 countries]\n3. Number of Countries with Population more than 1 Billion. [Only 2] [China and India]\n\nThere are more questions to answers. However, from tomorrow I will move to another dataset.\nYou can find kaggle kernel [here](https://www.kaggle.com/themlphdstudent/world-population-from-1955-to-2020).\n\nYou can find article of my progress on Day 1 - 3 on [medium](https://towardsdatascience.com/analysing-and-visualising-the-country-wise-population-from-1955-to-2020-with-pandas-matplotlib-70b3614eed6b).\n\n## Day 4 | 15 August 2020\n\nDay 4 of #100DaysofDataScience\nDataset : TMDB Box Office Prediction\n\nToday's questions are :\n1. Which movie made the highest revenue?[The Avengers, revenue - $ 1.5 Billion]\n2. Which movie is the most expensive? [Pirates of the Caribbean: On Stranger Tides, Budget - $ 380 Million]\n3. Which movie is the Longest? [ Carlos, 5hr 38min]\n4. In which year most movies were released? [2013, 141 movies are released]\n\nYou can find kaggle kernel [here](https://www.kaggle.com/themlphdstudent/box-office-revenue-analysis-and-visualization).\n\n## Day 5 | 16 August 2020\n\nDay 5 of #100DaysofDataScience\n\nDataset: TMDB Box Office Prediction\n\nToday's questions are :\n1. Which movie has highest popularity and lowest popularity?\n[Highest popularity --\u003e Wonder Woman] [Lowest Popularity --\u003e Big Time]\n2. In which month the highest number of movies are released from 1921 to 2017? [September month, a total of 362 movies released]\n3. On which date of the month most movies are released?[1st, a total of 152 movies are released]\n4. On which day of week most movies are released? [Friday, a total of 1334 movies released.]\n\nYou can find kaggle kernel [here](https://www.kaggle.com/themlphdstudent/box-office-revenue-analysis-and-visualization).\n\nYou can find article of my progress on Day 4 and 5 on [medium](https://towardsdatascience.com/box-office-revenue-analysis-and-visualization-ce5b81a636d7).\n\n## Day 6 | 17 August 2020\n\nDay 6 of #100DaysofDataScience\n\nDataset: Dataset: Countries Population from 1955 to 2020\n\nToday I created my first bar chart race plot. Which represents population change from 1955 to 2020.\n\n![](./resources/images/Population_Bar_Chart_Race.gif)\n\nYou can find kaggle kernel [here]().\n\n## Day 7 | 18 August 2020\n\nDay 7 of #100DaysofDataScience\nDataset: Lok Sabha Election Candidate list (2004 to 2019)\n\nToday's questions are :\n1. Youngest Candidate who participated in Lok Sabha Election?\n2. Candidate with the highest number of Criminal Cases.\n3. Which party has the highest number of total criminal cases?\n\nYou can find kaggle kernel [here]().\n\n## Day 8 | 19 August 2020\n\nDay 8 of #100daysofdatascience \nDataset: Lok Sabha Election Candidate list (2004 to 2019)\n\nToday, instead of finding an answer to the particular question on the combined dataset, I explore the dataset separately. It was a really great learning experience about data science and Indian politics :). Today's questions are:\n1. In which city the highest number of criminal cases are recorded against the candidates?\n2. Most common education among candidates?\n3. A number of candidates in each Political Party.\n\nYou can find kaggle kernel [here](https://www.kaggle.com/themlphdstudent/lok-sabha-candidate-data-analysis-visualization).\n\n## Day 9 | 20 August 2020\n\nToday I practice NumPy. Check out NumPy exercise Part 1 kaggle kernel [here](https://www.kaggle.com/themlphdstudent/learn-numpy-numpy-exercises-part-1).\n\n## Day 10 | 21 August 2020\n\nToday I practice NumPy. Check out NumPy exercise Part 1 kaggle kernel [here](https://www.kaggle.com/themlphdstudent/learn-numpy-numpy-exercises-part-1).\n\n## Day 11 | 22 August 2020\n\nDataset: Novel Corona Virus Dataset\n\nToday I started working on Corona Virus dataset. I checked some other kernels for reference.\nToday I cleaned dataset and perform some analysis. \n\nTomorrow I will do Visualization.\n\nYou can find kaggle kernel [here](https://www.kaggle.com/themlphdstudent/novel-corona-virus-covid19-eda-and-visualization).\n\n## Day 12 | 23 August 2020\n\nDataset: Novel Corona Virus Dataset\n\nToday I did USA coronavirus data analysis and learned how to create a waffle chart in python. There are so many things that I can learn from this dataset. Hence going to explore more on this data. \n\nYou can find kaggle kernel [here](https://www.kaggle.com/themlphdstudent/novel-corona-virus-covid19-eda-and-visualization).\n\n## Day 13 | 24 August 2020\n\nDataset: Novel Corona Virus Dataset\n\nToday I performed more visuals on covid dataset.\n\n1 month ago I created a Kaggle account and started working seriously in #kaggle and within a month I have become Kaggle notebook expert. \n\nI would like to thank all #kagglers for their support, love, and feedbacks which made me stronger day by day.\n\nThe more I learn, the more I realize that there's still so much more to learn but it's a really fun process. Still, a long way to go.\n\nYou can find kaggle kernel [here](https://www.kaggle.com/themlphdstudent/novel-corona-virus-covid19-eda-and-visualization).\n\n## Day 14 | 25 August 2020\n\nDataset: Campus Recruitment\n\n✅Analyze data\n✅Checked each feature. \n✅Checked the relation between some variables.\n\nYou can find kaggle kernel [here](https://www.kaggle.com/themlphdstudent/campus-recruitment-eda-classification).\n\n## Day 15 | 26 August 2020\n\nDataset: Campus Recruitment\n\n✅Developed Machine learning model to predict whether a candidate will get job or not. \n\nYou can find kaggle kernel [here](https://www.kaggle.com/themlphdstudent/campus-recruitment-eda-classification).\n\n## Day 16 | 27 August 2020\n\n## Day 17 | 28 August 2020\n\nToday I earned dataset expert badge on Kaggle. I become 2x Expert -- notebook and dataset.\n\nI would like to thank all #kagglers for their support, love, and feedbacks which made me stronger day by day.\n\nThe more I learn, the more I realize that there's still so much more to learn but it's a really fun process. Still, a long way to go.\n\n\n## Day 18 | 29 August 2020\n\nToday I started working on Matplotlib Cheatsheet. \nOn this kernel/notebook you will find all matplotlib plots.\n\nYou can find kaggle kernel [here](https://www.kaggle.com/themlphdstudent/cheatsheet-matplotlib-charts).\n\n## Day 19 | 30 August 2020\n\nUpdated more charts on matplotlib cheatsheet.\n\nYou can find kaggle kernel [here](https://www.kaggle.com/themlphdstudent/cheatsheet-matplotlib-charts).\n\n## Day 20 | 31 August 2020\n\nToday I started working first ever compitition on kaggle.\n\nDataset: Titanic Survive prediction\n\nToday I explore dataset and perform EDA.\n\nCheckout my kernel [here](https://www.kaggle.com/themlphdstudent/titanic-eda).\n\n## Day 21 | 1 September 2020\n\nDataset: Titanic Survive prediction\n\nToday I implemetend some machine learning models to predict where passenger will survive or not.\n\nCheckout my kernel [here](https://www.kaggle.com/themlphdstudent/titanic-eda).\n\n## Day 22 | 2 September 2020\n\nDataset: Titanic Survive prediction\n\nAfter exploring and builing predictive model today I prepared another kernel on tutorial for beginners.\nI might work more on this kernel tomorrow to make it more clear.\n\nCheckout my kernel [here](https://www.kaggle.com/themlphdstudent/titanic-survive-prediction-tutorial-for-beginners).\n\n## Day 23 | 3 September 2020\nDataset: Titanic Survive prediction\n\nAfter spending four days I think from tomorrow I will move to other dataset. I will work parallely on this kernel in my free time.\n\nCheckout my kernel [here](https://www.kaggle.com/themlphdstudent/titanic-survive-prediction-tutorial-for-beginners).\n\n## Day 24 | 4 September 2020\nDataset: Drug Classification\n\n- Explore Dataset\n- Checked for outliers in dataset\n- Correlation between some features\n\nCheckout kernel [here](https://www.kaggle.com/themlphdstudent/drug-classification-using-state-of-the-art-ml-algo).\n\n## Day 25 | 5 September 2020\nDataset: Drug Classification\n\n1. Data visualization\n2. Standardizing the data\n3. Deciding Models\n4. Splitting the data\n\nCheckout kernel [here](https://www.kaggle.com/themlphdstudent/drug-classification-using-state-of-the-art-ml-algo).\n\n## Day 26 | 6 September 2020\nDataset: Drug Classification\n\n1. Decision Tree and Random forest CLassifier\n2. Comparison of models\n3. Conclusion\n\nCheckout kernel [here](https://www.kaggle.com/themlphdstudent/drug-classification-using-state-of-the-art-ml-algo).\n\n## Day 27 to 30 | 7 to 10 September 2020\n\nDataset: Novel Corona Virus Dataset\nEach day, I worked on one country (USA, India, Italy, and Brazil). It is an amazing learning experience. Learned so many different visualization techniques such as Waffle chart, Bar race chart, Geographical visualization etc.)\n\nPlease have a look. Any constructive criticism in the comment section is highly welcomed.\n\nIf you find it helpful please upvote my work.\n\nYou can find kaggle kernel [here](https://www.kaggle.com/themlphdstudent/novel-corona-virus-covid19-eda-and-visualization).\n\n## Day 31 | 11 September 2020\n\nToday I started working on Seaborn Cheatsheet. \nOn this kernel/notebook you will find all seaborn plots.\n\nYou can find kaggle kernel [here](https://www.kaggle.com/themlphdstudent/cheat-sheet-seaborn-charts). \n\n## Day 32 | 12 September 2020\n\nUpdated more charts on seaborn cheatsheet.\n\nYou can find kaggle kernel [here](https://www.kaggle.com/themlphdstudent/cheat-sheet-seaborn-charts).\n\n## Day 33 | 13 September 2020\n\nToday I started working on some more numpy excercises. Numpy is very important python library.\n\nCheckout kernel [here](https://www.kaggle.com/themlphdstudent/learn-numpy-numpy-exercise-part-2).\n\n## Day 34 | 14 September 2020\n\nToday I continue working on numpy excerises. I am now more confident with numpy so today is my last day on numpy.\nFrom next week I will work on pandas library as pandas also very useful library.\n\nCheckout kernel [here](https://www.kaggle.com/themlphdstudent/learn-numpy-numpy-exercise-part-2).\n\n## Day 35 | 15 September 2020\n\n\n## Day 36 | 16 September 2020\n\n\n## Day 37 | 17 September 2020\n\n\n## Day 38 | 18 September 2020\n\n\n## Day 39 | 19 September 2020\n\n\n## Day 40 | 20 September 2020\n\n\n## Day 41 | 21 September 2020\n\n\n## Day 42 | 22 September 2020\n\n\n## Day 43 | 23 September 2020\n\n\n## Day 44 | 24 September 2020\n\n\n## Day 45 | 25 September 2020\n\n\n## Day 46 | 26 September 2020\n\n\n## Day 47 | 27 September 2020\n\n\n## Day 48 | 28 September 2020\n\n\n## Day 49 | 29 September 2020\n\n\n## Day 50 | 30 September 2020\n\n\n## Day 51 | 1 October 2020\n\n\n## Day 52 | 2 October 2020\n\n\n## Day 53 | 3 October 2020\n\n\n## Day 54 | 4 October 2020\n\n\n## Day 55 | 5 October 2020\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdurgeshsamariya%2F100daysofdatascience","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdurgeshsamariya%2F100daysofdatascience","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdurgeshsamariya%2F100daysofdatascience/lists"}