README.md updated file

# Analysis of Temperature Rise in PMSM 

This project aims at analysing how different parameters affect the temperature rise in the motor parts which would enable automotive industries to manufacture motors with parameters that are capable of maximizing the efficiency of the PMSM motor for Electric Vehicle (EV) application.

<img src="https://d1c4d7gnm6as1q.cloudfront.net/Pictures/480xany/4/7/1/10471_tn_csr-tq600-innotrans2014.jpg" align="center">

### Dataset

Dataset used can be dowloaded from [here](https://www.kaggle.com/wkirgsn/electric-motor-temperature) 

Libraries Involved: 

1. Numpy
2. Pandas
3. Pandas_profiling
4. matplotlib.pyplot
5. seaborn 

### Steps Involved: 

1. Importing Dependencies
2. Data Importing and Description.
3. Data Profiling.

   3.1.  Understanding the Dataset
   
   3.2.  Pre-Profiling


   3.3.  Pre-Processing


   3.4.  Post-Profiling
4. Data Visualization on the dataset.
5. Exploratory Data Analysis to analyse the effect of temperature rise

   5.1. Which parameters are highly correlated and negatively correlated with each other?

   5.2. Which profile id varies maximum and which one varies minimum for temperature rise for various parameters?

   5.3. Which profile id has highiest pm temperature?

   5.4. Which component of stator (from stator_yoke, stator winding, stator tooth) observes highiest variations for profile_id 20 (highiest varying profile_id)?
6. Conclusion 

### Conclusion
The analsyis can be summarised into the following: 

1. With the help of this notebook we learnt how exploratory data analysis can be carried out using Pandas plotting.
2. Also we have seen making use of packages like matplotlib and seaborn to develop better insights about the data.
3. We have also seen how pre-proceesing helps in dealing with missing values and irregualities present in the data. However, in this dataset, there were no missing values.
4. We also make use of pandas profiling feature to generate an html report containing all the information of the various features present in the dataset.
5. We have seen the impact of columns like stator_winding, stator_yoke, stator_tooth, pm,and profile_id on the temperature.
6. The most important inference drawn from all this analysis is, we get to know what are the features on which temperature is highly positively and negatively correlated with.

This analysis will help us to choose which machine learning model we can apply to predict survival of test dataset.