SGP WEEKLY REPORT

 SGP WEEKLY REPORT

PROJECT NAME :

BENGALURU HOUSE PRICE PREDICTION

GUIDED BY : 

MS. REKHA KARANGIYA

PROJECT BY :

RAHUL BHAVSAR (D20IT155)

VAGH SURESH (D20IT154)

PURPOSE OF THIS PROJECT

• Our project is a Machine learning model/app, which will guess the accurate price of your future house on the basis of some certain specification of your future house.
• Predicting house prices is expected to help people who plan to buy a  house so they can know the price range in the future, then they can plan their finances well. In addition, house price predictions are also beneficial for property investors to know the trend of housing prices in a certain location.
• So we want to create one ml model that can be used to predict the price of this house. It gives 90% right prediction but accurate prediction about the price of the house because we train that model with all the previous year data which predict the approx but accurate price of that house using this ML model.

COLLECTING THE DATA

• The first step is to collect the dataset to train the model. I got the dataset from a website called “ kaggle.com “ which provides so many different datasets for creating machine learning models. here is the screenshot of the website.

        

• For our project, I got the data called  “Bengaluru_House_price.csv” which is the raw file for the dataset. using that we train our model

         

 

         

• For model creation, we use the Anaconda (jupyter notebook). here is the raw file of the dataset that we downloaded from the Kaggle app.

MODEL CREATION

• STEP 1: We start performing the model on that .csv file and create "House_Price_Predictions.ipynb".

• STEP 2: we start our data science work on this data set. First of all, we import all the packages that we needed to implement on this model. after that using info we check the datatypes of the data set

• STEP 3: After info() we Apply value count on all the features to check the outliers or the whitespace and other 

 

• STEP 4: putting Value count we apply isna.sum() to find how many null values our features have.

• STEP 5: After this, we use data. describe() to check min, max, and avg values of the bath and price which will help us to remove outliers from them.

• STEP 6: Using data.info we check how many missing values and outlier's we have in our data set.

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