shekharp77/housing

Project Name

In this project a housing componay is planning to enter the market of Australia and need us to build a model that can help us buying the right property.

Table of Contents

• General Info
• Technologies Used
• Conclusions
• Acknowledgements

General Information

• This is a ML project that contains a Jupyter notebook where we did data analysis and created a model.
• You are required to model the price of houses with the available independent variables. This model will then be used by the management to understand how exactly the prices vary with the variables.
• They can accordingly manipulate the strategy of the firm and concentrate on areas that will yield high returns.

Conclusions

Below are the things thats affects the housing prices most Important features and their coefficients: Feature Coefficient 15 GrLivArea 0.117245 3 OverallQual 0.100235 5 YearBuilt 0.049465 25 GarageCars 0.040594 2 LotArea 0.039039 4 OverallCond 0.031049 12 1stFlrSF 0.026937 8 BsmtFinSF1 0.023620 6 YearRemodAdd 0.022298 16 BsmtFullBath 0.016182 158 Foundation_PConc 0.014690 208 Functional_Typ 0.014348 195 CentralAir_Y 0.013927 71 Neighborhood_NridgHt 0.013236 23 Fireplaces 0.011693 61 Neighborhood_Crawfor 0.011120 81 Condition1_Norm 0.009763 120 Exterior1st_BrkFace 0.009555 251 SaleType_New 0.009252 77 Neighborhood_StoneBr 0.008313 170 BsmtExposure_Gd 0.007475 26 GarageArea 0.007163 27 WoodDeckSF 0.004402 50 LotConfig_CulDSac 0.003780 70 Neighborhood_NoRidge 0.003677 169 BsmtCond_TA 0.003636 31 ScreenPorch 0.003459 28 OpenPorchSF 0.001663 258 SaleCondition_Partial 0.001218 76 Neighborhood_Somerst 0.001146 11 TotalBsmtSF 0.000489 46 LandContour_HLS 0.000417 217 GarageType_CarPort -0.000063 44 LotShape_IR3 -0.000363 96 BldgType_Duplex -0.000772 172 BsmtExposure_No -0.001475 119 Exterior1st_BrkComm -0.001527 188 Heating_Grav -0.002079 207 Functional_Sev -0.002209 203 Functional_Maj2 -0.002758 211 FireplaceQu_None -0.004173 62 Neighborhood_Edwards -0.004994 202 KitchenQual_TA -0.005942 21 KitchenAbvGr -0.008787 152 ExterQual_TA -0.009323 39 MSZoning_RM -0.009821 91 Condition2_PosN -0.012338 236 PoolQC_Gd -0.013343

Technologies Used

• Python 3.9
• Seaborn
• matplotlib
• Pandas
• Numpy
• Tabulate

Acknowledgements

Give credit here.

• stackoverflow.com
• Analytics vidya https://www.analyticsvidhya.com/
• Towards data science https://towardsdatascience.com/