Kumarans Data Academy

When we use Python for data analysis, machine learning, or any programming, we do not write each and every code. Why Should we? Some experts already wrote code for us and packed it as a Library. This reusable collection of code is called a Python Library . A library in Python is like a ready-made toolbox. Instead of building every tool yourself, you take the tools from the toolbox and start using it in your code. Why do we need Python Libraries? Save time – We do not write the same code again and again. Easy to use – Just install, import, and start using. Powerful features – Some libraries can do complex tasks in one line of code. Community support – Big communities maintain them, so they keep improving. Examples of Python Data Science Libraries: Pandas – Works with data tables like Excel, filtering and summarizing data NumPy – Works with numbers, arrays, mathematical calculations Matplotlib – Creates graphs and charts Scikit-learn – For machine learning models like regres...

Have you ever noticed in your Life that One thing affects another thing? If Someone Study Well, they score High Marks If Someone Eat Well, they gain Weight If Someone Sleep Well, they remain Younger Doing One thing affects Another thing. But, How much? How much One thing affects Another thing? 50%? 90%? or only 20%? To find that, we use Linear Regression method. Linear Regression Method uses this Formula:  Y = MX + C What is Y: Y is also called as 'Dependent' Value. Because it depends on X. If X Changes, Y Changes. So Y is Dependent value. So we always assign Dependent Column to Variable Y is Python. example: We have 2 Columns. "Study Hours" and "Exam Score" Now, does the Exam score increase when Study Hour increase? YESS! If Someone study more time, their score will obviously increase. So 'Exam Score' Column is depending on 'Study Hours' Column, Right? We call 'Exam Score' Column as Y Column. Because, it is dependent on another Colum...

Mean: Its nothing but Average.  Well, what is Average? When you see some scoring Not Higher, Not Lower, but medium marks like 50, 60 etc.. we call them average student. Example : Now, I have 5 Students Marks: 10,40,51,68,98 What is the Average mark of this 5 Students? 53.4 is the Average score of 5 students. How this is calculated? (10+40+51+68+98) / Number of values. How many values I have? 10,40,51,68,98 - I have 5 Values. So,  (10+40+51+68+98) / 5 267 / 5 = 53. 4 Mode: It is the number that is repeating more times in the group of numbers. Example:  I have money in my Pocket. 1 - 500 rupees Note, 3 - 100 Rupees Note, 7 - 20 Rupees Note. Now, tell me which Note I have the Most? 20 Rupees Right? because I have 7 20 rupees Notes.  Exactly, that is how we check Mode. If you are given data with numbers (1,2,3,4,5,5,5,5,5,5,6,7,8,9) You know which is Mode. It is 5, because it is the most available number in this group. Median: It is the central Point of data.  You a...

As a Data Professional, Instead of showing data in a sentence or table, which is hard to read, You can show Data using suitable graph in Excel, Power BI, Tableau Etc. for better understanding  Column Chart Column Chart is made of Vertical Bars. Each Bar shows Each value.  Column Chart is mostly used to show the performance of an Employee every year, every month, etc... You can also use this chart to show Sales, Quantity, Improvement by time period. It works best if you have X axis = Timeline & Y axis = Quantity / Value. Bar Chart: Bar Chart is made of Horizontal Bars. Each Bar shows Value of Each Category. You can use this Chart to show Data by Category Like Products: TV, Mobile, Fridge. It helps analyze which is a Top Category and which is a Lowest Category. It works best if you have X axis = Value & Y axis = Category. Line Chart: It is used to show Trends over time. Internet Service provider Companies can check how the Internet speed in your area changes every Second...

 A Chart is a Visual Representation of Data Trends, Patterns and Insights.  Its like you are looking at a picture and understanding what is happening with the data. Same like watching a Movie instead of reading the Movie story. Why? Chart gives Fast, Better and Simple Understanding of a Big Complex Data. When you become a Data Analyst or Data Scientist in future. You will present your Data with Charts. Your Manager, Program Head, Clients, Directors.. they don't have time to read the entire data from a excel file or PowerPoint file.  A Chart will make it easier for them to understand the Situation, Progress, Profit, Loss, Improvement, Danger etc.. within few seconds of looking at the Charts. There are around 100 Charts available in Data Science World. Each has its own purpose. When you create Multiple Charts in a single page, it is called Report. When you build Multiple pages of report, it is called Dashboards.  If you create Best Quality & Easy to Understand Char...

Regression models are a type of machine learning model used to predict. It tries to find a relationship between columns.  Regression Models are ready-made mathematical formulas created to make life easier . 1. Linear Regression: Linear Regression is a model that checks whether there is a relationship between two or more columns. The relationship means: if one column value increases, will the other column value also increase or decrease. If you study more hours, your marks will increase. If you have more work experience, your salary will increase. These are called positive relationships. Example: Students Mark - It can change over time, It is a Numerical score. Sales - It can change over time, It is a Numerical Count. Temperature - It can change over time. It is Number based. Here is a Simple Linear Regression Graph: If you have a Students Marks & Study time data for past 1 year, you can predict future marks using Linear Regression. 2. Logistic Regression: It is used for Pr...