Machine Learning- Logistic Regression

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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 gap of different size of the data.

Encoding a categorical columns:

We can't just ignore the categorical data so we encode the data into numerical value to data classifical/data prediction algorithm using OneHotEncoder.

Conclusion:

After completing the Exploratory  data analysis, Visualization, and Preprocessing and finally we create Logistic regression model . Check the scores of the model what we have created. we are in beggining stage of creating a model so, I am not going to confuse you by dumping everything. In our next post we will discuss detail about the above mentioned steps with decision tree classifier and random forest classifier . Source couse of logistic regression model:Logistic-Regression-models-sourse code.

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