Predicting the future isn't magic, it's Artificial Intelligence.

Introduction: Last time what we saw is Linear regression which is helpfull in predicting the data which is in certain patern but Logistic regression helpfull in classification problem. For example, whether the person having diseases or not ,whether will rain tommorow or not. Exploraory data analysis: In this phase we usually check the rows and columns of the given data sets and describe it and check wheather the data containing missing value or not. Seperate the numerical columns and categorical columns and split the data into train,val,test test to generalize the model prediction.  Data Visualization : Here we are analysing the co-relation of data. which is helpfull in building the better model. Actually I build a logistic regression model on breast cancer detection and weather prediction which is good for the people who are beginning the carrier in data science. Data Preprocessing : Imputing: Filling the missing value with appropriate technique. Scaling: Scaling is used to reduce the

Hi, guys this is my first machine learning model using linear regression... Introduction: In this model we will predict the insurance amount to be paid by the new customer from a given datasets. Myself download the ACME insurance datasets from the github you can try any other datasets. Let's predict the insurance amount paid by the new customers. Relation between the data: Let's analysis the some of the data using visualization technique. By analyzing the data there is a strong relation between smoker and charges. Scikit learn: It is one of the powerfull machine learning library in the field of data science. In this problem we use this to calculate linear regression(y=mx+c) model, and also using OneHotEncoder to convert multiple object into calculatable values. By understanding the line is just above the cluster this is due to the outliers. This is the simple regression line once we find for single varriable we can easily do it for multiple variable using scikit-learn. Loss: On