Rinester88/ML-Libraries

The libraries discussed here widely, one can do ML using python. And applying these libraries in a dataset discussed here widely. We can notice that using replace function we can easily change the values of column. Using sns.countplot() we can visualize the count of how many times repeated of all values of a particular column. Describe() function tells us columns count, mean, sd, min, max etc. Using seaborn boxplot we can visualize that a box plot is a way to show the spread and centers of a data set. Using seaborn histplot we can visualize that histogram represent the data distribution by forming bins along the range of the data and then drawing bars to show the number of observations that fall in each bin. Using pd.dataframe we can create a new dataframe of some features. Using train_test_split we can split the main dataset into training and testing dataset. We can transform all values of some columns using MinMaxScaler which scales all the data features in the range [0, 1] and StandardScaler which makes mean = 0 and scales the data to unit variance. Using seaborn histplot we can visualize that we use MinMaxScaler for directly normalizing the input variables and use StandardScaler for directly standardizing the input variables. Using count() function we count total number of values of particular column. Using value_counts() function we count column's all values how many times repeated. Using append() function we can add dataframe. Seaborn is a siimple graphical tool. Seaborn can be used for both exploration and presentation of findings. Basically here we learned about some machine learning libraries and we praticed on train_test split and normalisation.

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