Data Report

A data report communicates insights derived from data, not just numbers. A good report tells a logical, evidence-based story that supports decision-making.

Section Main Goal
Title & Introduction Define context and purpose
Business Logic Explain why the analysis matters
Data Summary Describe the dataset
Group & Sort Show data organization logic
Visualizations Communicate insights visually
Drill Down Explore details and exceptions
Conclusions Provide insights and actions
References Support transparency
Footnotes Give credit and clarifications

1. Title and Introduction

Set the context and tell the reader what the report is about and why it matters.

What to include

Title: House Price Prediction Analysis

Introduction:

2. Business Logic

Explain why the analysis exists from a business or real-world perspective.

What to include

Accurate house price prediction helps real estate agencies optimize pricing strategies and assists buyers in making informed investment decisions.

3. Data Summary

Help readers understand what data was used.

What to include

Import CSV to SQLite

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import pandas as pd
import sqlite3

# ==============================
# CONFIGURATION
# ==============================
CSV_PATH = "housing.csv"
DB_PATH = "housing.db"
TABLE_NAME = "houses"

# ==============================
# READ CSV
# ==============================
df = pd.read_csv(CSV_PATH)

# ==============================
# CONNECT TO SQLITE
# ==============================
conn = sqlite3.connect(DB_PATH)

# ==============================
# WRITE TO SQLITE
# ==============================
df.to_sql(
    TABLE_NAME,
    conn,
    if_exists="replace",  # overwrite table if exists
    index=False
)

# ==============================
# VERIFY INSERT
# ==============================
cursor = conn.cursor()
cursor.execute(f"SELECT COUNT(*) FROM {TABLE_NAME}")
row_count = cursor.fetchone()[0]

print(f"Imported {row_count:,} rows into table '{TABLE_NAME}'")

conn.close()

Anlysis data:

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iimport sqlite3

# ==============================
# CONFIGURATION
# ==============================
DB_PATH = "housing.db"
TABLE_NAME = "houses"
DATASET_NAME = "Housing market dataset"
TARGET_VARIABLE = "price"

# ==============================
# CONNECT TO DATABASE
# ==============================
conn = sqlite3.connect(DB_PATH)
cursor = conn.cursor()

# ==============================
# NUMBER OF RECORDS
# ==============================
cursor.execute(f"SELECT COUNT(*) FROM {TABLE_NAME}")
num_records = cursor.fetchone()[0]

# ==============================
# NUMBER OF FEATURES (COLUMNS)
# ==============================
cursor.execute(f"PRAGMA table_info({TABLE_NAME})")
columns_info = cursor.fetchall()
num_features = len(columns_info)

# ==============================
# IDENTIFY NUMERIC VS CATEGORICAL
# ==============================
numeric_types = ("INT", "INTEGER", "REAL", "FLOAT", "DOUBLE")
numeric_count = 0
categorical_count = 0

for col in columns_info:
    col_type = col[2].upper()
    if any(t in col_type for t in numeric_types):
        numeric_count += 1
    else:
        categorical_count += 1

# ==============================
# OUTPUT DATA SUMMARY
# ==============================
print(f"Dataset: {DATASET_NAME}")
print(f"Records: {num_records:,} properties")
print(
    f"Features: {num_features} variables "
    f"({numeric_count} numeric, {categorical_count} categorical)"
)
print("Target variable: House price")

# ==============================
# CLOSE CONNECTION
# ==============================
conn.close()

4. Group By and Sort Criteria

Explain how data was aggregated or organized to reveal patterns.

What to include

Data was grouped by district and sorted by average house price to identify high-value and low-value areas.

4.1. Grouping Variables

Trong pandas, grouping variables được truyền vào groupby().

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df.groupby("district")
df.groupby("year")
df.groupby(["district", "house_type"])

4.2. Average House Price by District (Sort Descending)

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avg_price_by_district = (
    df.groupby("district")["price"]
      .mean()
      .sort_values(ascending=False)
)

avg_price_by_district.head()

4.3 Group By + Multiple Aggregations (Best Practice)

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district_summary = (
    df.groupby("district")
      .agg(
          avg_price=("price", "mean"),
          median_price=("price", "median"),
          num_properties=("price", "count")
      )
      .sort_values(by="avg_price", ascending=False)
)

district_summary.head()

4.4. Top-K Most Expensive Districts

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top_5_districts = district_summary.head(5)
top_5_districts


Hoặc gộp trực tiếp:

top_5_districts = (
    df.groupby("district")["price"]
      .mean()
      .sort_values(ascending=False)
      .head(5)
)

4.5. Average Price Trend by Year (Sort Ascending)

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price_trend_by_year = (
    df.groupby("year")["price"]
      .mean()
      .sort_index()
)

price_trend_by_year

4.6. Group By Multiple Variables (District + House Type)

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district_type_summary = (
    df.groupby(["district", "house_type"])
      .agg(
          avg_price=("price", "mean"),
          num_properties=("price", "count")
      )
      .sort_values(by="avg_price", ascending=False)
)

district_type_summary.head()

5. Visualizations

Make insights easy to understand and compare.

What to include

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import matplotlib.pyplot as plt
import seaborn as sns

plt.figure(figsize=(8, 5))
sns.histplot(df['price'], bins=30, kde=True)

plt.title('Distribution of House Prices')
plt.xlabel('House Price')
plt.ylabel('Frequency')

plt.show()


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plt.figure(figsize=(10, 6))
sns.boxplot(x='district', y='price', data=df)

plt.title('House Prices by District')
plt.xlabel('District')
plt.ylabel('House Price')
plt.xticks(rotation=45)

plt.show()


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import numpy as np

# Select numeric columns only
numeric_df = df.select_dtypes(include=np.number)

# Compute correlation matrix
corr_matrix = numeric_df.corr()

plt.figure(figsize=(10, 8))
sns.heatmap(
    corr_matrix,
    annot=True,
    fmt=".2f",
    cmap="coolwarm",
    square=True
)

plt.title('Correlation Heatmap of Numeric Features')
plt.show()

6. Drill Down

Allow deeper exploration of specific segments or anomalies.

What to include

After identifying District A as having the highest average prices, a drill-down analysis was conducted by house type and size.

Drill down analysis is the process of:

moving from high-level summaries to detailed views in order to understand why certain segments show unusual patterns or anomalies.

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All houses
 └── District
      └── House type
           └── Individual properties / Outliers

Standard Drill Down Process (3 Steps)

Step 1: Identify Anomalies at a High Level

Question: Which groups show unusually high or low values?

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district_summary = (
    df.groupby("district")
      .agg(
          avg_price=("price", "mean"),
          count=("price", "count")
      )
      .sort_values("avg_price", ascending=False)
)

district_summary.head()

High-level insight: District A has the highest average house price.

Step 2: Subgroup Analysis

Question: Within District A, which subgroups drive the high prices?

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target_district = district_summary.index[0]

district_drill = (
    df[df["district"] == target_district]
      .groupby("house_type")
      .agg(
          avg_price=("price", "mean"),
          count=("price", "count")
      )
      .sort_values("avg_price", ascending=False)
)

district_drill

Intermediate insight: Apartments contribute most to the high prices in District A.

Step 3: Deep Drill Down (Outliers and Special Cases)

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q1 = df["price"].quantile(0.25)
q3 = df["price"].quantile(0.75)
iqr = q3 - q1

outliers = df[
    (df["price"] < q1 - 1.5 * iqr) |
    (df["price"] > q3 + 1.5 * iqr)
]

outliers.head()

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district_outliers = outliers[outliers["district"] == target_district]
district_outliers.sort_values("price", ascending=False).head()

Detailed insight: A small number of luxury properties significantly inflate the average price.

7. Conclusions and Recommendations

Translate analysis into actionable insights.

What to include

Larger living area and location are the strongest predictors of house price. Recommendation: prioritize these features in pricing models and valuation tools.

Step 1: Summarize Key Findings

(What happened?)

Guidelines

Example

Step 2: Explain Business or Practical Implications

(So what?)

Translate each finding into real-world meaning.

Guidelines

Example

Step 3: Provide Clear Recommendations

(What should be done?)

Convert insights into specific, actionable actions.

Guidelines

Example

Step 4: State Limitations of the Analysis

(What should we be careful about?)

Acknowledge important constraints to ensure transparency and credibility.

Guidelines

Example

8. References and Appendices

Ensure credibility, transparency, and reproducibility.

What to include

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References
[1] Author, A. (Year). Title. Source.
[2] Kaggle. (2023). Housing Prices Dataset. https://www.kaggle.com/
[3] ...