Data Science Tips for Beginners!

  Q1. You are given a train data set having 1000 columns and 1 million rows. The data set is based on a classification problem. Your manager has asked you to reduce the dimension of this data so that model computation time can be reduced. Your machine has memory constraints. What would you do? (You are free to make practical assumptions.) Answer: Processing a high dimensional data on a limited memory machine is a strenuous task, your interviewer would be fully aware of that. Following are the methods you can use to tackle such situation: Since we have lower RAM, we should close all other applications in our machine, including the web browser, so that most of the memory can be put to use. We can randomly sample the data set. This means, we can create a smaller data set, let’s say, having 1000 variables and 300000 rows and do the computations. To reduce dimensionality, we can separate the numerical and categorical variables and remove the correlated variables. For numerical variable...

  Data preprocessing  is a step in the data mining and data analysis process that takes raw data and transforms it into a format that can be understood and analyzed by computers and machine learning. When we talk about  data , we usually think of some  large datasets  with some rows and columns. While that is a likely scenario, it is not always the case — data could be in so many different forms:  Structured Tables, Images, Audio files, Videos , etc. Machines don’t understand text, image, or video data as it is, they understand 1s and 0s. Real-world data also contains noises, missing values, etc. which cannot be directly used for ML models. Hence,  data preprocessing  is required for cleaning the data and making it suitable for an ML model which increases the accuracy and efficiency of the model. It involves the following steps: Getting the Dataset Importing Libraries Importing Dataset Data Quality Assessment: i) Finding and Processing Missing/Inc...

  List of Common Machine Learning Algorithms Here is the list of commonly used machine learning algorithms. These algorithms can be applied to almost any data problem: Linear Regression Logistic Regression Decision Tree SVM Naive Bayes kNN K-Means Random Forest Dimensionality Reduction Algorithms Gradient Boosting algorithms GBM XGBoost LightGBM CatBoost 1. Linear Regression It is used to estimate real values (cost of houses, number of calls, total sales etc.) based on continuous variable(s). Here, we establish relationship between independent and dependent variables by fitting a best line. This best fit line is known as regression line and represented by a linear equation Y= a *X + b. https://www.analyticsvidhya.com/wp-content/uploads/2015/08/Linear_Regression.png 2. Logistic Regression Don’t get confused by its name! It is a classification not a regression algorithm. It is used to estimate discrete values ( Binary values like 0/1, yes/no, true/false ) based on given set of indepe...

  1 . Sentiment Analyzer of Social Media This is one of the interesting and innovative machine learning projects. As, social media like Facebook, Twitter, and YouTube is the ocean of big data. Therefore, mining these data can be beneficial in a number of ways to understand user sentiments and opinions. This project can be effective for digital marketing and branding to understand the opinion or reaction for a product or service of a customer. 2 . Music Recommendation System Are you a lover of music? Always love to listen to your favorite one? Then, you will be glad to know about this interesting machine learning project idea. This can also be an innovative project. The goal of this project is to recommend music based on user listening history. 3 . Credit Card Fraud Detection Project Companies that involve a lot of transactions with the use of cards need to find anomalies in the system. The project aims to build a fraud detection model on credit cards. We will use the transaction an...

  1. Pick the brain of an expert. There are myriads of ways to learn data science. You can read articles, watch videos, enroll in onli n e courses, turn up at meetups, etc. But one thing that you cannot “learn” is the  experience . That you have to gain throughout years of working in the field. There is much to learn from Data science experts, their experience in managing end-to-end machine learning and deep learning projects, their philosophy when constructing a data science team from scratch,

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This Python Cheat is from the Book Python Crash Course which aims to remind you of syntax rules and grasp all the important concepts in Python programming as a beginner. You will also

This Python Cheat Sheet presents the Python basics that you need to do data science and will guide you through variables and data types, Strings, Lists, to eventually land at the fundamental package for scientific

One of the ways to build a robust and intelligent chatbot system is to feed question answering datasets during training the model. Question answering systems provide real-time answers that are essential and can be said

Title  Python Data Science Handbook: Essential Tools for Working with Data Author(s)   Jake VanderPlas Publisher:  O'Reilly Media; 1 edition (December 10, 2016); eBook (2018. Updated continuously) License(s):  Text Content:  CC-BY-NC-ND ; Source Code:  MIT License Paperback  548 pages Book Description:

Fundamentals of Python Programming by Richard L. Halterman Book Description: This book focuses on

It is so good to be interested about Data Science and Machine Learning. Many students think that Data Science is a non technical subject and people with zero coding knowledge, no logical thinking, no creativity can pursue data science courses and can become a Data Scientist. To all those students, looking for building a good portfolio for Data Science, here are some tips to follow: 1. Certifications and courses are not going to take you

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