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AyodeleOjo/Nigeria-Real-Estate-Project

Domain:

socioeconomic

Record type:

dataset
Creator:
Ayo
Host:
My Data Science Project. Nigeria-Real-Estate-Project (Data Sourcing ). In a bit to replicate what I have done while working on the first two projects in the Applied Data Science Course at WorldQuant University— where I applied the data wrangling and visualization skills that I learnt, in examining the real estate market in Mexico(First Project), and also created a machine learning model that predicts apartment prices in Buenos Aires, Argentina(Second Project). I went on Kaggle to source for a dataset to this effect. In my search, I stumbled on the CSV file— "nigeria_houses_data.csv" uploaded by Abdullahi Yunus. lnkd.in This dataset contains House listings in Nigeria and their prices based on Location and other parameters. Datashape (24326, 8)==> 24,326 Observations (rows) and 8 Parameters (columns) . PARAMETERS: ~‌Bedrooms -> number of bedrooms in the houses ‌~ Bathrooms -> number of bathrooms in the houses ‌~ Toilets -> number of toilets ‌~ Parking_space ‌~ Title -> house type ‌~ Town -> town in which the house is located ‌~ State -> state within Nigeria in which the house is located and finally ‌~ Price -> the target column. Now that our Dataset is ready, be on the look out for my next post on how I progressed on the project to creating a machine learning model that predicts the price of Detached Duplex in Lagos State, Nigeria . #datascience #machinelearning #project #realestate #visualization #data #kaggle # Nigeria-Real-Estate-Project In a bit to replicate what I have done while working on the first two projects in the Applied Data Science Course at WorldQuant University— where I applied the data wrangling and visualization skills that I learnt, in examining the real estate market in Mexico(First Project), and also created a machine learning model that predicts apartment prices in Buenos Aires, Argentina(Second Project). __Operations performed are:__ __Data Sourcing:__ I went on Kaggle to source for a dataset to this effect. In my search, I stumbled on the CSV file— "nigeria_houses_data.csv" uploaded by Abdullahi Yunus. lnkd.in This dataset contains House listings in Nigeria and their prices based on Location and other parameters. Datashape (24326, 8)==> 24,326 houses (rows) and 8 parameters (columns) . __Data Preparation__ * Data wrangling & visualization * Subset Data to Detached Duplex in Lagos __Exploration & Feature Engineering__ * Data visualization * Columns dropping * Features selection * Split Data __Model Building__ * Test-Train Split * Transformed categorical variables using OneHotEncoder * Create Model (Linear Regression Model ) * Evaluate Model __Model Deployment__ * Using a function * Using an interactive dashboard