Feature scaling-Standardization

all the data sets are brought to mean =0 and standard deviation=1;

 datasets are brought to orifgin i.e mean=0

 it is also called Z-distribution

refer:untitled7 in jupyter notebook

Comments

Post a Comment

Popular posts from this blog

proceduce for data analysis some basic functions

 1.df.shape() : to print row and columns 2.df.head() 3.df.sample(5): to print sample of data with random data,5 is for printing 5 data rows 4. df.info(): - to find data type of data                                object: string                              intrger: int64(consumes less memory than float)                              float: float64                    - to find not null value in particular column                   - memory space occupied by data 4. df.isnull().sum()                    -to find total no. of missing value in all the column 5 df.describe(): to visualize data in mean std, deviation ...
Read more

working with json and sql data

 to read json data:    df=pd.read_json("train.json") to real sql data:     sql data consists query that have insert command and those command you need to fetch in your ide of python using xampp server    step 1: download the data to your local machine   step 2: open xampp server   step 3: create database and upload the file of your designed file   step 4: install sql conector in your ide by typing the given code              !pip install mysql.connector   step 5: import mysql.connector   step 6: conn=mysql.connector.connect(host='localhost',user='root',password='',database='world')       this creates an object of sql format : localhost= host ip name, user=name of database, password=database password   step 7: pd.read_sql_query("SELECT * FROM CITY",conn)       select * from city is the query you want to show you can show any rquery as you want a...
Read more