100 days ML

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

 numerical numerical combination  

 it is the analysis done with a single column to know the information of single column for categorical data 1.countplot/bargraph  simply it is bar graph plot plotted \  for eg. if you want to find out how many passenger were male in titanic died and how many females were female                 import seaborn as sns                  sns . countplot ( df [ 'sex' ])        you can get it numerically through:          df['Survived'].value_counts()       df['Embarked'].value_counts().plot(kind='bar') 2. to watch in pie chart      df['Embarked'].value_counts().plot(kind='pie')         this doesn't show percentage inside : to watch percentage:     df [ ' Embarked ' ] . value_counts () . plot ( kind = 'pie' , autopct = ' %.2f ' ) for numerical data        1. histo...

 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 ...

 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...

 1. CSV files         i. reading csv files from local storage                df=pd.read_csv('healthcare-dataset-stroke-data.csv')        ii. making your own column instead of pandas default column               df=pd.read_csv('healthcare-dataset-stroke-data.csv',index_col='id')       iii. reading file from some link            import requests            from io import StringIO  url = "link name" headers = {"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10.14; rv:66.0) Gecko/20100101 Firefox/66.0"} req = requests.get(url, headers=headers) data = StringIO(req.text) pd.read_csv(data)    iv.if there is no column name or column name become as a temple becoming first row value      pd.read_csv('test.csv',header=1) v.if you want to select only some of the colum...