Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,97 +1,161 @@
-Installing packages into ‘/usr/local/lib/R/site-library’
-(as ‘lib’ is unspecified)
-
-also installing the dependency ‘vroom’
-
-
-Warning message in install.packages(c("readr", "dplyr", "ggplot2", "corrplot")):
-“installation of package ‘ggplot2’ had non-zero exit status”
-
-Attaching package: ‘dplyr’
-
-
-The following objects are masked from ‘package:stats’:
-
-    filter, lag
-
-
-The following objects are masked from ‘package:base’:
-
-    intersect, setdiff, setequal, union
-
-
-corrplot 0.95 loaded
-
-Rows: 52820 Columns: 5
-── Column specification ────────────────────────────────────────────────────────
-Delimiter: ","
-dbl (5): price, surface, rooms, price_m2, neighborhood_score
-
-ℹ Use `spec()` to retrieve the full column specification for this data.
-ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
-[1] "Dataset loaded successfully"
-A tibble: 6 × 5
-price	surface	rooms	price_m2	neighborhood_score
-<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
-121000	69	2	1753.623	6.200
-246000	49	2	5020.408	5.015
-318050	115	6	2765.652	6.350
-163000	42	2	3880.952	6.350
-150000	93	5	1612.903	6.350
-153000	46	2	3326.087	5.935
-spc_tbl_ [52,820 × 5] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
- $ price             : num [1:52820] 121000 246000 318050 163000 150000 ...
- $ surface           : num [1:52820] 69 49 115 42 93 46 32 83 70 25 ...
- $ rooms             : num [1:52820] 2 2 6 2 5 2 2 4 3 1 ...
- $ price_m2          : num [1:52820] 1754 5020 2766 3881 1613 ...
- $ neighborhood_score: num [1:52820] 6.2 5.02 6.35 6.35 6.35 ...
- - attr(*, "spec")=
-  .. cols(
-  ..   price = col_double(),
-  ..   surface = col_double(),
-  ..   rooms = col_double(),
-  ..   price_m2 = col_double(),
-  ..   neighborhood_score = col_double()
-  .. )
- - attr(*, "problems")=<externalptr> 
-     price            surface           rooms           price_m2     
- Min.   :      1   Min.   :  1.00   Min.   : 0.000   Min.   :     0  
- 1st Qu.: 185000   1st Qu.: 40.00   1st Qu.: 2.000   1st Qu.:  3193  
- Median : 290000   Median : 62.00   Median : 3.000   Median :  4800  
- Mean   : 405173   Mean   : 69.16   Mean   : 3.116   Mean   :  7332  
- 3rd Qu.: 464598   3rd Qu.: 88.00   3rd Qu.: 4.000   3rd Qu.:  8341  
- Max.   :2990000   Max.   :482.00   Max.   :24.000   Max.   :666667  
- neighborhood_score
- Min.   : 3.885    
- 1st Qu.: 6.727    
- Median : 7.780    
- Mean   : 7.870    
- 3rd Qu.: 9.290    
- Max.   :20.504    
-
-
-
-
-
-Call:
-lm(formula = price ~ surface + rooms + neighborhood_score, data = train)
-
-Residuals:
-     Min       1Q   Median       3Q      Max 
--1815140  -145637   -68531    35243  2767231 
-
-Coefficients:
-                     Estimate Std. Error t value Pr(>|t|)    
-(Intercept)        -467418.10   10358.66  -45.12   <2e-16 ***
-surface               4932.35      76.81   64.21   <2e-16 ***
-rooms               -29954.53    2059.06  -14.55   <2e-16 ***
-neighborhood_score   79383.79    1120.02   70.88   <2e-16 ***
----
-Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
-
-Residual standard error: 341000 on 42252 degrees of freedom
-Multiple R-squared:  0.2284,	Adjusted R-squared:  0.2283 
-F-statistic:  4169 on 3 and 42252 DF,  p-value: < 2.2e-16
-[1] "RMSE: 339999.604607665"
-
-[1] "Model results saved successfully"
+import streamlit as st
+import pandas as pd
+import matplotlib.pyplot as plt
+
+# ---------------------------------------------------
+# PAGE CONFIG
+# ---------------------------------------------------
+
+st.set_page_config(
+    page_title="Île-de-France Housing Price Estimator",
+    page_icon="🏠",
+    layout="wide"
+)
+
+# ---------------------------------------------------
+# LOAD DATA
+# ---------------------------------------------------
+
+df = pd.read_csv("housing_analysis_dataset-3.csv")
+
+# ---------------------------------------------------
+# MODEL COEFFICIENTS (from R)
+# ---------------------------------------------------
+
+INTERCEPT = -467418.10
+COEF_SURFACE = 4932.35
+COEF_ROOMS = -29954.53
+COEF_NEIGHBORHOOD = 79383.79
+
+
+def predict_price(surface, rooms, neighborhood_score):
+
+    price = (
+        INTERCEPT
+        + COEF_SURFACE * surface
+        + COEF_ROOMS * rooms
+        + COEF_NEIGHBORHOOD * neighborhood_score
+    )
+
+    return price
+
+
+# ---------------------------------------------------
+# TITLE
+# ---------------------------------------------------
+
+st.title("🏠 Île-de-France Housing Price Estimator")
+
+st.write(
+"""
+This dashboard estimates housing prices using a **linear regression model**
+trained on the **DVF real estate dataset**.
+"""
+)
+
+# ---------------------------------------------------
+# SIDEBAR INPUTS
+# ---------------------------------------------------
+
+st.sidebar.header("Property characteristics")
+
+surface = st.sidebar.slider(
+    "Surface (m²)",
+    20,
+    300,
+    70
+)
+
+rooms = st.sidebar.slider(
+    "Number of rooms",
+    1,
+    10,
+    3
+)
+
+neighborhood_score = st.sidebar.slider(
+    "Neighborhood score",
+    3.0,
+    10.0,
+    7.0
+)
+
+# ---------------------------------------------------
+# PREDICTION
+# ---------------------------------------------------
+
+if st.sidebar.button("Estimate price"):
+
+    price = predict_price(surface, rooms, neighborhood_score)
+
+    st.subheader("Estimated Property Price")
+
+    st.success(f"{int(price):,} €")
+
+
+# ---------------------------------------------------
+# DATASET OVERVIEW
+# ---------------------------------------------------
+
+st.write("---")
+st.header("Dataset Overview")
+
+col1, col2, col3 = st.columns(3)
+
+col1.metric("Number of properties", len(df))
+col2.metric("Average price", f"{int(df['price'].mean()):,} €")
+col3.metric("Average price per m²", f"{int(df['price_m2'].mean()):,} €")
+
+
+# ---------------------------------------------------
+# PRICE DISTRIBUTION
+# ---------------------------------------------------
+
+st.write("---")
+st.header("Price Distribution")
+
+fig, ax = plt.subplots()
+
+ax.hist(df["price"], bins=50)
+
+ax.set_xlabel("Price (€)")
+ax.set_ylabel("Number of properties")
+
+st.pyplot(fig)
+
+
+# ---------------------------------------------------
+# PRICE VS SURFACE
+# ---------------------------------------------------
+
+st.write("---")
+st.header("Price vs Surface")
+
+fig2, ax2 = plt.subplots()
+
+ax2.scatter(df["surface"], df["price"], alpha=0.3)
+
+ax2.set_xlabel("Surface (m²)")
+ax2.set_ylabel("Price (€)")
+
+st.pyplot(fig2)
+
+
+# ---------------------------------------------------
+# MODEL EXPLANATION
+# ---------------------------------------------------
+
+st.write("---")
+st.header("Model")
+
+st.write("The prediction is based on the following linear regression model:")
+
+st.latex(
+r'''
+Price =
+-467418
++ 4932 \times Surface
+- 29954 \times Rooms
++ 79383 \times NeighborhoodScore
+'''
+)