Machine Learning Course Session 04 Part 01 | Learn Data Cleaning or Preprocessing Like a Pro!
Introduction :
Data Cleaning is a crucial step in data preprocessing, ensuring that your data is accurate, complete, and ready for analysis. In this comprehensive video, we dive deep into the essentials of data cleaning, breaking down common techniques, best practices, and challenges you may face during the preprocessing stage. Whether you're a beginner or looking to refine your skills, this tutorial will guide you through each step, from identifying dirty data to cleaning it for better accuracy and analysis. By mastering these data cleaning techniques, you'll streamline your data science workflow, avoid common pitfalls, and make your datasets more reliable for future analysis. Don't miss out on this critical aspect of data preprocessing that can make or break your data-driven projects!
Importan points about today's session :
1) Data Preprocessing (or Cleaning) Steps which include :
a) Reading Data (Means Loading Data from different sources)
b) Exploring Data ( means o make data more meaning full or get insights from data) / Data Insights
c) Cleasing Data (means Cleaning data)
d) Outlier Detection and Removing
e) Data Transformtion (means Normalize data / Rescale Data)
f) Categorical into Numerical
g) Dimensinlaity Reduction (PCA) ( which include creating new features or droping features)
h) Handling Impblanced Data
i) Feature Selection
j) Data Splitting or Model Fitting
2) Importance of Data Preprocessing
3) Requiment Gathering & Problem Identifications by discussion
4) Discussed Problem Description on Upwork
5) Matplotlib & Seaborn used for visualization
6) Numpy used for scientific computing
7) Pandas used for Data processing
8) pd.read_csv or pd.read_html or pd.read_json or pd.read_xml and so on
9) data.head ( ) first five rows or data.head ( 10 ) display top 10 rows and so on
10) data.tail ( ) used to show last rows or data.tail ( 5) show last five rows
11) data.info , it shows all featurs their corresponding rows count , data types , null values etc
12) data.shape used to show total number of rows and columns
13) data.sample for random display
14) Discusssed about the tasks to differentiate Data Analysis and Data Pre-procession
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