Introduction:

Property management in Nigeria is plagued by inefficiencies, from manual rent collection to delayed maintenance requests.

=> Inefficiencies and high operational costs => Poor communication and tenant dissatisfaction => Data management issues and lack of insights => Maintenance and repair delays PROJECT AIM This project aims to design a smart property management platform to improve the experience for property managers, landlords, and tenants.

In [74]:

Importing Libery

import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns

from sklearn.preprocessing import OneHotEncoder from sklearn.preprocessing import StandardScaler, MinMaxScaler from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestRegressor from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import GridSearchCV from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, confusion_matrix, roc_curve In [2]:

loading the dataset

df = pd.read_csv('Smart_Property_Management _Dataset.csv')

Display basic information about the dataset

df_info = df.info() df_head = df.head()

display(df_info, df_head)

Descriptive Statistics

numerical_summary = df.describe() categorical_summary = df.describe(include=['object'])

display(numerical_summary, categorical_summary)

Identify and drop irrelevant features

irrelevant_features = ['User_ID', 'Property_ID'] df = df.drop(columns = irrelevant_features, errors = 'ignore')

df.head()# To check the change effect

convertingvariable to appropriate datatype

df['Age'] = pd.to_numeric(df['Age'], errors='coerce', downcast='float')

df.head() :

Handle missing values

df.select_dtypes(include = ['object']).columns: df[columns].fillna(df[columns]).mode()[0], inplace = True)

df['Sex'].fillna(df['Sex'].mode()[0], inplace=True) df['Used_Property_Management_Software'].fillna(df['Used_Property_Management_Software'].mode()[0], inplace=True) df['Devices_Used_Most'].fillna(df['Devices_Used_Most'].mode()[0], inplace=True) df['Preferred_Contact_Method'].fillna(df['Preferred_Contact_Method'].mode()[0], inplace=True) df['Family_status'].fillna(df['Family_status'].mode()[0], inplace=True) df['Thoughts_On_Smart_Home_Integration'].fillna(df['Thoughts_On_Smart_Home_Integration'].mode()[0], inplace=True) df['Property_Management_Service_Need_Improvement'].fillna(df['Property_Management_Service_Need_Improvement'].mode()[0], inplace=True) df['Used_Property_Management_Software'].fillna(df['Used_Property_Management_Software'].mode()[0], inplace=True) df['Preferred_Payment_Method'].fillna(df['Preferred_Payment_Method'].mode()[0], inplace=True) df['State'].fillna(df['State'].mode()[0], inplace=True) df['Remainder_Report_Duration'].fillna(df['Remainder_Report_Duration'].mode()[0], inplace=True) df['Country'].fillna(df['Country'].mode()[0], inplace=True) df['City'].fillna(df['City'].mode()[0], inplace=True) df['Type'].fillna(df['Type'].mode()[0], inplace=True) df['Property_size'].fillna(df['Property_size'].median(), inplace=True) df['Years_Of_Experience'].fillna(df['Years_Of_Experience'].median(), inplace=True)

Verify changes

df.info() missing_values = df.isnull().sum() print(missing_values) df.head()

Visualizations

Age Distribution

plt.figure(figsize=(10, 6)) sns.histplot(df['Age'], bins=20, kde=True) plt.title('Age Distribution') plt.xlabel('Age') plt.ylabel('Frequency') plt.show()

:

Property Size Distribution

plt.figure(figsize=(10, 6)) sns.histplot(df['Property_size'].dropna(), bins=20, kde=True) plt.title('Property Size Distribution') plt.xlabel('Property Size') plt.ylabel('Frequency') plt.show()

Devices Used Most Distribution

plt.figure(figsize=(10, 6)) sns.countplot(y='Devices_Used_Most', data=df) plt.title('Devices Used Most Distribution') plt.xlabel('Count') plt.ylabel('Devices Used Most') plt.show() display(df)

Perform one-hot encoding

df_encoded = pd.get_dummies(df, columns=['Used_Property_Management_Software', 'Devices_Used_Most', 'Sex', 'Preferred_Contact_Method', 'Family_status', 'Thoughts_On_Smart_Home_Integration', 'Property_Management_Service_Need_Improvement', 'Preferred_Payment_Method', 'Remainder_Report_Duration', 'Country', 'State', 'City', 'Type'], drop_first=True)

display(df_encoded)

One-hot encoding of categorical variables

categorical_columns = df_encoded.select_dtypes(include=['object']).columns one_hot_encoder = OneHotEncoder(sparse=False, drop='first') encoded_categorical_data = one_hot_encoder.fit_transform(df[categorical_columns])

Create a DataFrame for the encoded categorical variables

encoded_categorical_df_encoded = pd.DataFrame( encoded_categorical_data, columns=one_hot_encoder.get_feature_names_out(categorical_columns) )

Drop original categorical columns and concatenate encoded columns

dataset = df_encoded.drop(columns=categorical_columns) dataset = pd.concat([df_encoded, encoded_categorical_df_encoded], axis=1)