Capstone 4 Infographic

Capstone Summary

This portfolio document outlines the Capstone 4 project within an applied data science curriculum, focusing on data visualization techniques using Python. The primary objective involves transforming prior statistical analysis into visual artifacts, specifically utilizing the Matplotlib and Pandas libraries to generate charts from health survey data. Key deliverables include categorical bar charts for regional and health distributions, a correlation matrix heatmap, and several scatter plots exploring relationships between income, age, and medical visits. The source documentation emphasizes reproducibility by providing structured code notebooks, automated data loading paths, and a centralized output directory for saved images. Furthermore, the materials include analytical observations that distinguish between genuine data trends and perfect correlations resulting from derived variables.

Capstone 4 Scope

Capstone 4 turns the prior analysis work into required saved visuals, making the notebook, chart exports, and written observations reviewable together in one PHP page.

Primary notebook input: Capstone 2/outputs/NSMES1988updated.csv.

Notebook evidence, plot artifacts, and code screenshots.

Original Project PDF

The original Capstone 4 directions are embedded here.

Open Viewer Download PDF

Requirement Checklist

Import relevant Python libraries necessary for Python and Pandas analysis, as well as visualization.

Source mapping: Notebook setup and imports

Evidence note: The notebook imports `pandas` for grouped summaries and `matplotlib.pyplot` for plot generation and PNG export.

Import the CSV file `NSMES1988updated.csv` and create a new dataframe for working with Pandas.

Source mapping: Notebook load step

Evidence note: The notebook loads the Capstone 2 updated handoff dataset and ensures the derived year and dollar columns are available.

Indicate the plotting library choice and reasons for the choice.

Source mapping: Notebook C4-T3

Evidence note: The notebook explicitly records Matplotlib as the plotting library and explains its export and customization benefits.

Plot the Week 3 categorical data for Health and Region.

Source mapping: Notebook C4-T4

Evidence note: The notebook saves `region_counts.png`, `health_counts.png`, and `mean_visits_region_health.png` under `outputs/plots/`.

Plot the Week 4 analyses and correlation.

Source mapping: Notebook C4-T5

Evidence note: The notebook exports the correlation matrix plus three scatter plots for the required relationship analysis.

Prepare a detailed report and record your observations.

Source mapping: Notebook markdown and conclusions

Evidence note: The notebook markdown summarizes plot observations and records the final conclusion with saved-artifact notes.

Requirement Walkthrough

Each walkthrough block maps one required visualization step to the notebook section, the saved artifacts, and the interpretation notes recorded in the notebook.

Set Up The Visualization Notebook And Load The Updated Dataset

Notebook section: Setup, imports, and load cells

Requirement: Import the required analysis and visualization libraries and load `NSMES1988updated.csv`.

Capstone 4 starts from the Capstone 2 updated dataset and brings in the minimum library set needed to build, show, and save the required charts.

Results Capture

Libraries used: `pandas` and `matplotlib.pyplot`.
Main input: `Capstone 2/outputs/NSMES1988updated.csv`.
The notebook ensures `age_years` and `income_dollars` exist before generating chart inputs.

import pandas as pd
import matplotlib.pyplot as plt

DEFAULT_DATASET = "NSMES1988updated.csv"
dataset_path = resolve_dataset_path(DEFAULT_DATASET)
df = pd.read_csv(dataset_path)

if "age_years" not in df.columns and "age" in df.columns:
    df["age_years"] = (df["age"] * 10).round(0)
if "income_dollars" not in df.columns and "income" in df.columns:
    df["income_dollars"] = (df["income"] * 10000).round(0)

State The Plotting Library Choice

Notebook section: C4-T3

Requirement: Indicate the plotting library choice and reasons for the choice.

The notebook records the plotting stack before any charts are produced so the page can map the tool choice directly to the PDF instruction.

Results Capture

Library choice: Matplotlib.
Reasons recorded in the notebook: direct pandas compatibility, flexible formatting, and reliable PNG export for grading artifacts.
This requirement records the plotting-library choice and justification.

print("Plotting library: matplotlib")
print("Reason: direct pandas compatibility, flexible formatting, and reliable PNG export for grading artifacts.")

Plot Health And Region Analysis

Notebook section: C4-T4

Requirement: Plot the Week 3 categorical analysis for Health and Region.

Capstone 4 converts the Week 3 categorical findings into saved bar charts and a grouped region-health utilization chart.

Results Capture

Saved categorical plot artifacts: `region_counts.png`, `health_counts.png`, and `mean_visits_region_health.png`.
The notebook notes that the `other` region has the largest count and `average` health dominates the sample.
The grouped region-health chart preserves the segmented visits story from the Capstone 3 pivot work.

region_counts = df["region"].value_counts()
fig, ax = plt.subplots(figsize=(7, 4.5))
region_counts.sort_index().plot(kind="bar", title="Count by Region", ax=ax)
fig.savefig(PLOTS_DIR / "region_counts.png", dpi=150)

fig, ax = plt.subplots(figsize=(7, 4.5))
df["health"].value_counts().sort_index().plot(kind="bar", title="Count by Health Category", ax=ax)
fig.savefig(PLOTS_DIR / "health_counts.png", dpi=150)

pivot_visits = pd.pivot_table(df, index="region", columns="health", values="visits", aggfunc="mean")
fig, ax = plt.subplots(figsize=(8, 5))
pivot_visits.plot(kind="bar", ax=ax)
fig.savefig(PLOTS_DIR / "mean_visits_region_health.png", dpi=150)

Associated Chart

Count by Region

Saved bar chart for the region frequency breakdown.

View Download

Associated Chart

Count by Health Category

Saved bar chart for the health category distribution.

View Download

Associated Chart

Mean Visits by Region and Health

Grouped chart tying the Week 3 categorical analysis to average visit levels.

View Download

Plot Correlation And Relationship Analysis

Notebook section: C4-T5

Requirement: Plot the Week 4 analysis and correlation.

The notebook then moves from grouped categorical views into numeric correlation structure and scatter-relationship plots for the requested analysis layer.

Results Capture

Saved correlation and scatter artifacts: `correlation_matrix.png`, `scatter_income_vs_visits.png`, `scatter_age_vs_visits.png`, `scatter_income_vs_emergency.png`.
Notebook correlation highlights include `emergency~hospital=0.476`, `ovisits~novisits=0.467`, and `visits~chronic=0.262`.
The notebook also records the expected `1.0` correlation for the derived pairs `age~age_years` and `income~income_dollars`.

num_df = df.select_dtypes(include=["number"])
corr = num_df.corr(numeric_only=True)

fig, ax = plt.subplots(figsize=(10, 8))
cax = ax.imshow(corr, cmap="coolwarm", vmin=-1, vmax=1)
fig.colorbar(cax, ax=ax)
fig.savefig(PLOTS_DIR / "correlation_matrix.png", dpi=150)

for x, y, fname, title in [
    ("income_dollars", "visits", "scatter_income_vs_visits.png", "Income vs Visits"),
    ("age_years", "visits", "scatter_age_vs_visits.png", "Age vs Visits"),
    ("income_dollars", "emergency", "scatter_income_vs_emergency.png", "Income vs Emergency Visits"),
]:
    fig, ax = plt.subplots(figsize=(6.5, 4.5))
    ax.scatter(df[x], df[y], alpha=0.3, s=12)
    fig.savefig(PLOTS_DIR / fname, dpi=150)

Associated Chart

Correlation Matrix

Heatmap used to scan the full numeric correlation structure.

View Download

Associated Chart

Income vs Visits

Scatter plot for the income and total visits relationship.

View Download

Associated Chart

Age vs Visits

Scatter plot for the age and total visits relationship.

View Download

Associated Chart

Income vs Emergency Visits

Scatter plot for the income and emergency visits relationship.

View Download

Record The Final Report And Observations

Notebook section: Final conclusions

Requirement: Prepare a detailed report and record your observations.

The notebook finishes by converting the saved chart outputs into an explicit observation set so the page can present both the figures and the interpretation layer.

Results Capture

The notebook confirms all required visualization artifacts are saved under `outputs/plots/`.
The final notes distinguish true relationships from derived-feature correlations that are mechanically perfect.
The page can therefore surface both the plots and the notebook’s written caveats without inventing new claims.

print("Capstone 4 completed: C4-T3 to C4-T5")
print("Artifacts saved under outputs/plots")

Charts and Plots

Charts generated by the notebook. Click any image to open full-size, or use the download button to save the file.

Count by region chart

Download

Count by health category chart

Download

Mean visits by region and health chart

Download

Correlation matrix heatmap

Download

Income versus visits scatter plot

Download

Age versus visits scatter plot

Download

Income versus emergency visits scatter plot

Download

Open the staged plot artifact generated by the Capstone 4 notebook.

View Download

Colab Notebook

This section provides the notebook preview, launch link, and project file links.

Saved plot files and notebook output provide the execution record for this capstone.

Embedded Notebook Preview

Cell 1 Markdown

Capstone 4 — Session 4: Data Visualization Techniques

Run timestamp: 2026-02-19 01:49:22

Goal

I converted Week 3 and Week 4 analysis into required visual outputs and documented interpretation-ready findings.
I generated and saved all required plots for categorical comparisons and correlation/relationship analysis.

Inputs

NSMES1988updated.csv from Capstone 2/outputs/

Outputs

All exports go to ./outputs/ (and plots to ./outputs/plots/ when applicable)

Libraries (documented)

pandas: I used it to prepare grouped summaries and correlation inputs for plotting.
matplotlib: I used it to build and save all required charts to outputs/plots/.

Key dataset note

age is encoded as Age in years (divided by 10) (e.g., 6.9 = 69 years).

Cell 2 Markdown

C?-T0 — Runtime setup (paths + output folders)

I used this setup cell first to initialize reproducible paths and plotting output folders.

Cell 3 Code · python

from pathlib import Path
from datetime import datetime

try:
    from IPython.display import display as _ipy_display
except Exception:
    _ipy_display = None

def show(value):
    if _ipy_display is not None:
        _ipy_display(value)
    else:
        print(value)

# --- Project metadata ---
CAPSTONE = 4
SESSION_TITLE = 'Session 4: Data Visualization Techniques'

print(f"Capstone: {CAPSTONE} | Session: {SESSION_TITLE}")
print("Run timestamp:", datetime.now().strftime("%Y-%m-%d %H:%M:%S"))

CWD = Path.cwd()
if (CWD / f"Capstone {CAPSTONE}").exists():
    BASE_DIR = CWD / f"Capstone {CAPSTONE}"
elif CWD.name == f"Capstone {CAPSTONE}":
    BASE_DIR = CWD
elif (CWD / "Incremental_Capstone" / f"Capstone {CAPSTONE}").exists():
    BASE_DIR = CWD / "Incremental_Capstone" / f"Capstone {CAPSTONE}"
else:
    BASE_DIR = CWD

# --- Paths ---
def first_existing_path(candidates):
    for candidate in candidates:
        candidate = Path(candidate).expanduser()
        if candidate.exists():
            return candidate
    return None

def resolve_dataset_path(default_filename: str) -> Path:
    """Resolve dataset path from known local runtime locations (non-interactive)."""
    path = first_existing_path([
        BASE_DIR / default_filename,
        CWD / default_filename,
        CWD / "Incremental_Capstone" / f"Capstone {CAPSTONE}" / default_filename,
    ])
    if path is None:
        path = BASE_DIR / default_filename

    if not path.exists():
        raise FileNotFoundError(f"Dataset not found: {path}")
    return path

OUTPUT_DIR = BASE_DIR / "outputs"
PLOTS_DIR = OUTPUT_DIR / "plots"
OUTPUT_DIR.mkdir(exist_ok=True)
PLOTS_DIR.mkdir(parents=True, exist_ok=True)

print("Output directory:", OUTPUT_DIR)
print("Plots directory:", PLOTS_DIR)

Output

Capstone: 4 | Session: Session 4: Data Visualization Techniques
Run timestamp: 2026-02-19 00:32:05
Output directory: c:\DEV_Projects\SIMPLILEARN\CAPSTONE_Applied_Data_Science_with _Python\Incremental_Capstone\Capstone 4\outputs
Plots directory: c:\DEV_Projects\SIMPLILEARN\CAPSTONE_Applied_Data_Science_with _Python\Incremental_Capstone\Capstone 4\outputs\plots

Cell 4 Markdown

C?-T1 — Imports (ONLY what you use)

I documented each import with why I used it and what it enabled in this capstone.

Cell 5 Code · python

import pandas as pd  # DataFrames + aggregation/pivot/correlation preparation
import matplotlib.pyplot as plt  # Plot creation and export to outputs/plots

Cell 6 Markdown

C?-T2 — Load dataset

I loaded the instructed handoff dataset from Capstone 2 using default + fallback paths.

Cell 7 Code · python

DEFAULT_DATASET = "NSMES1988updated.csv"

try:
    dataset_path = resolve_dataset_path(DEFAULT_DATASET)
except FileNotFoundError:
    fallback = first_existing_path([
        BASE_DIR.parent / "Capstone 2" / "outputs" / "NSMES1988updated.csv",
        CWD / "Capstone 2" / "outputs" / "NSMES1988updated.csv",
        CWD / "Incremental_Capstone" / "Capstone 2" / "outputs" / "NSMES1988updated.csv",
    ])
    if fallback is None:
        raise
    dataset_path = fallback
    print("Local default not found; using fallback:", dataset_path)

df = pd.read_csv(dataset_path)
if "age_years" not in df.columns and "age" in df.columns:
    df["age_years"] = (df["age"] * 10).round(0)
if "income_dollars" not in df.columns and "income" in df.columns:
    df["income_dollars"] = (df["income"] * 10000).round(0)

print("Loaded:", dataset_path)
print("Shape:", df.shape)
show(df.head())

Output

Local default not found; using fallback: c:\DEV_Projects\SIMPLILEARN\CAPSTONE_Applied_Data_Science_with _Python\Incremental_Capstone\Capstone 2\outputs\NSMES1988updated.csv
Loaded: c:\DEV_Projects\SIMPLILEARN\CAPSTONE_Applied_Data_Science_with _Python\Incremental_Capstone\Capstone 2\outputs\NSMES1988updated.csv
Shape: (4406, 20)

   visits  nvisits  ovisits  novisits  emergency  hospital   health  chronic  \
0       5        0        0         0          0         1  average        2   
1       1        0        2         0          2         0  average        2   
2      13        0        0         0          3         3     poor        4   
3      16        0        5         0          1         1     poor        2   
4       3        0        0         0          0         0  average        2   

       adl region  age  gender married  school  income employed insurance  \
0   normal  other  6.9    male     yes       6  2.8810      yes       yes   
1   normal  other  7.4  female     yes      10  2.7478       no       yes   
2  limited  other  6.6  female      no      10  0.6532       no        no   
3  limited  other  7.6    male     yes       3  0.6588       no       yes   
4  limited  other  7.9  female     yes       6  0.6588       no       yes   

  medicaid  age_years  income_dollars  
0       no         69           28810  
1       no         74           27478  
2      yes         66            6532  
3       no         76            6588  
4       no         79            6588

Cell 8 Markdown

C?-T3 — Validation checks

Confirm expected columns exist
Confirm key dtypes
Check missing values

Results Capture:

What I did: I validated expected schema/dtypes and checked missing values prior to plotting.
What I found: shape=(4406, 20) and no missing values; plotting fields required for tasks are available.
Caveats: derived pairs (age vs age_years, income vs income_dollars) naturally dominate top correlation rankings.

Cell 9 Code · python

expected_cols = [
    "visits", "nvisits", "ovisits", "novisits", "emergency", "hospital",
    "health", "chronic", "adl", "region", "age", "gender",
    "married", "school", "income", "employed", "insurance", "medicaid",
    "age_years", "income_dollars"
]

missing_cols = [c for c in expected_cols if c not in df.columns]
print("Missing expected columns:", missing_cols)

print("\nDtypes:")
show(df.dtypes)

print("\nMissing values (count):")
na_counts = df.isna().sum().sort_values(ascending=False)
show(na_counts[na_counts > 0] if (na_counts > 0).any() else na_counts.head())

Output

Missing expected columns: []

Dtypes:

visits              int64
nvisits             int64
ovisits             int64
novisits            int64
emergency           int64
hospital            int64
health                str
chronic             int64
adl                   str
region                str
age               float64
gender                str
married               str
school              int64
income            float64
employed              str
insurance             str
medicaid              str
age_years           int64
income_dollars      int64
dtype: object

Missing values (count):

visits       0
nvisits      0
ovisits      0
novisits     0
emergency    0
dtype: int64

Cell 10 Markdown

C4-T3 — State plotting library choice + reasons

PDF requirement: State which library you used for plotting and why.

What I completed

I used Matplotlib for all required visualizations and file exports.

Results Capture

Library choice: Matplotlib.
Reasons: strong pandas compatibility, clear customization, and straightforward PNG export to outputs/plots/.

Code evidence

The next cell prints the plotting-library choice summary used in this submission.

Cell 11 Code · python

print("Plotting library: matplotlib")
print("Reason: direct pandas compatibility, flexible formatting, and reliable PNG export for grading artifacts.")

Output

Plotting library: matplotlib
Reason: direct pandas compatibility, flexible formatting, and reliable PNG export for grading artifacts.

Cell 12 Markdown

C4-T4 — Plot Week 3 categorical analysis for Health and Region

PDF requirement: Plot the Week 3 categorical analysis for Health and Region.

What I completed

I transformed Week 3 category analysis into saved bar-chart visualizations for region and health, plus a grouped region-health visits chart.

Results Capture

Saved plot artifacts:
outputs/plots/region_counts.png
outputs/plots/health_counts.png
outputs/plots/mean_visits_region_health.png
Interpretation highlights:
other region has the largest count, while west has the smallest.
average health status dominates the sample compared with excellent and poor.
Mean visits vary across region-health segments.

Artifacts

outputs/plots/region_counts.png (example)
outputs/plots/health_*.png

Code evidence

The next cell contains all categorical plotting commands I ran and saved.

Cell 13 Code · python

# Plot 1: counts by region
if "region" in df.columns:
    region_counts = df["region"].value_counts()
    fig, ax = plt.subplots(figsize=(7, 4.5))
    region_counts.sort_index().plot(kind="bar", title="Count by Region", ax=ax)
    ax.set_xlabel("Region")
    ax.set_ylabel("Count")
    out = PLOTS_DIR / "region_counts.png"
    fig.tight_layout()
    fig.savefig(out, dpi=150)
    plt.show()
    print("Saved:", out)

# Plot 2: counts by health
if "health" in df.columns:
    fig, ax = plt.subplots(figsize=(7, 4.5))
    df["health"].value_counts().sort_index().plot(kind="bar", title="Count by Health Category", ax=ax)
    ax.set_xlabel("Health")
    ax.set_ylabel("Count")
    out = PLOTS_DIR / "health_counts.png"
    fig.tight_layout()
    fig.savefig(out, dpi=150)
    plt.show()
    print("Saved:", out)

# Plot 3: mean visits by region and health
if set(["region", "health", "visits"]).issubset(df.columns):
    pivot_visits = pd.pivot_table(df, index="region", columns="health", values="visits", aggfunc="mean")
    fig, ax = plt.subplots(figsize=(8, 5))
    pivot_visits.plot(kind="bar", ax=ax)
    ax.set_title("Mean Visits by Region and Health")
    ax.set_xlabel("Region")
    ax.set_ylabel("Mean Visits")
    out = PLOTS_DIR / "mean_visits_region_health.png"
    fig.tight_layout()
    fig.savefig(out, dpi=150)
    plt.show()
    print("Saved:", out)

Output

<Figure size 700x450 with 1 Axes>

Saved: c:\DEV_Projects\SIMPLILEARN\CAPSTONE_Applied_Data_Science_with _Python\Incremental_Capstone\Capstone 4\outputs\plots\region_counts.png

<Figure size 700x450 with 1 Axes>

Saved: c:\DEV_Projects\SIMPLILEARN\CAPSTONE_Applied_Data_Science_with _Python\Incremental_Capstone\Capstone 4\outputs\plots\health_counts.png

<Figure size 800x500 with 1 Axes>

Saved: c:\DEV_Projects\SIMPLILEARN\CAPSTONE_Applied_Data_Science_with _Python\Incremental_Capstone\Capstone 4\outputs\plots\mean_visits_region_health.png

Cell 14 Markdown

C4-T5 — Plot Week 4 analysis + correlation

PDF requirement: Plot Week 4 analysis and correlation. Provide detailed report and observations.

What I completed

I computed numeric correlations, exported a correlation-matrix figure, and generated required scatter plots for key relationships.

Results Capture

Correlation highlights: emergency~hospital=0.476, ovisits~novisits=0.467, visits~chronic=0.262.
Derived pairs age~age_years and income~income_dollars reached 1.0 as expected.
Saved artifacts: correlation_matrix.png, scatter_income_vs_visits.png, scatter_age_vs_visits.png, scatter_income_vs_emergency.png.

Artifacts

outputs/plots/correlation_matrix.png
outputs/plots/scatter_*.png

Code evidence

The next cell contains all correlation and scatter plotting commands I executed.

Cell 15 Code · python

num_df = df.select_dtypes(include=["number"])
corr = num_df.corr(numeric_only=True)
show(corr)

fig, ax = plt.subplots(figsize=(10, 8))
cax = ax.imshow(corr, cmap="coolwarm", vmin=-1, vmax=1)
fig.colorbar(cax, ax=ax, fraction=0.046, pad=0.04)
ax.set_xticks(range(len(corr.columns)))
ax.set_yticks(range(len(corr.columns)))
ax.set_xticklabels(corr.columns, rotation=90)
ax.set_yticklabels(corr.columns)
ax.set_title("Correlation Matrix (Numeric Features)")

out = PLOTS_DIR / "correlation_matrix.png"
fig.tight_layout()
fig.savefig(out, bbox_inches="tight", dpi=150)
plt.show()
print("Saved:", out)

scatter_specs = [
    ("income_dollars", "visits", "scatter_income_vs_visits.png", "Income vs Visits"),
    ("age_years", "visits", "scatter_age_vs_visits.png", "Age vs Visits"),
    ("income_dollars", "emergency", "scatter_income_vs_emergency.png", "Income vs Emergency Visits")
]

for x, y, fname, title in scatter_specs:
    if x in df.columns and y in df.columns:
        fig, ax = plt.subplots(figsize=(6.5, 4.5))
        ax.scatter(df[x], df[y], alpha=0.3, s=12)
        ax.set_title(title)
        ax.set_xlabel(x)
        ax.set_ylabel(y)
        out = PLOTS_DIR / fname
        fig.tight_layout()
        fig.savefig(out, dpi=150)
        plt.show()
        print("Saved:", out)

Output

                  visits   nvisits   ovisits  novisits  emergency  hospital  \
visits          1.000000  0.226365  0.068144  0.078468   0.158748  0.240789   
nvisits         0.226365  1.000000  0.001129  0.041768   0.049056  0.050401   
ovisits         0.068144  0.001129  1.000000  0.466923   0.065388  0.110573   
novisits        0.078468  0.041768  0.466923  1.000000   0.024083  0.065301   
emergency       0.158748  0.049056  0.065388  0.024083   1.000000  0.476061   
hospital        0.240789  0.050401  0.110573  0.065301   0.476061  1.000000   
chronic         0.261886  0.037113  0.100805  0.040748   0.203845  0.233524   
age             0.003404 -0.041640 -0.040028 -0.032813   0.061023  0.074122   
school          0.064433  0.085974 -0.012156 -0.011733  -0.071192 -0.036082   
income         -0.004951  0.010731 -0.004267 -0.007786  -0.026462 -0.014183   
age_years       0.003404 -0.041640 -0.040028 -0.032813   0.061023  0.074122   
income_dollars -0.004951  0.010731 -0.004267 -0.007786  -0.026462 -0.014183   

                 chronic       age    school    income  age_years  \
visits          0.261886  0.003404  0.064433 -0.004951   0.003404   
nvisits         0.037113 -0.041640  0.085974  0.010731  -0.041640   
ovisits         0.100805 -0.040028 -0.012156 -0.004267  -0.040028   
novisits        0.040748 -0.032813 -0.011733 -0.007786  -0.032813   
emergency       0.203845  0.061023 -0.071192 -0.026462   0.061023   
hospital        0.233524  0.074122 -0.036082 -0.014183   0.074122   
chronic         1.000000  0.099758 -0.065829 -0.047397   0.099758   
age             0.099758  1.000000 -0.142996 -0.073130   1.000000   
school         -0.065829 -0.142996  1.000000  0.259199  -0.142996   
income         -0.047397 -0.073130  0.259199  1.000000  -0.073130   
age_years       0.099758  1.000000 -0.142996 -0.073130   1.000000   
income_dollars -0.047397 -0.073130  0.259199  1.000000  -0.073130   

                income_dollars  
visits               -0.004951  
nvisits               0.010731  
ovisits              -0.004267  
novisits             -0.007786  
emergency            -0.026462  
hospital             -0.014183  
chronic              -0.047397  
age                  -0.073130  
school                0.259199  
income                1.000000  
age_years            -0.073130  
income_dollars        1.000000

<Figure size 1000x800 with 2 Axes>

Saved: c:\DEV_Projects\SIMPLILEARN\CAPSTONE_Applied_Data_Science_with _Python\Incremental_Capstone\Capstone 4\outputs\plots\correlation_matrix.png

<Figure size 650x450 with 1 Axes>

Saved: c:\DEV_Projects\SIMPLILEARN\CAPSTONE_Applied_Data_Science_with _Python\Incremental_Capstone\Capstone 4\outputs\plots\scatter_income_vs_visits.png

<Figure size 650x450 with 1 Axes>

Saved: c:\DEV_Projects\SIMPLILEARN\CAPSTONE_Applied_Data_Science_with _Python\Incremental_Capstone\Capstone 4\outputs\plots\scatter_age_vs_visits.png

<Figure size 650x450 with 1 Axes>

Saved: c:\DEV_Projects\SIMPLILEARN\CAPSTONE_Applied_Data_Science_with _Python\Incremental_Capstone\Capstone 4\outputs\plots\scatter_income_vs_emergency.png

Cell 16 Markdown

Final section — Conclusions (required)

I completed the required visualization deliverables using the instructed Capstone 2 handoff dataset.
I produced and interpreted categorical analysis charts for health and region.
I delivered correlation and scatter-relationship visuals with caveats documented for derived-feature pairs.
I saved all grading artifacts under outputs/plots/ and updated WORK_SUMMARY.md accordingly.

Cell 17 Code · python

print("Capstone 4 completed: C4-T3 to C4-T5")
print("Artifacts saved under outputs/plots")

Output

Capstone 4 completed: C4-T3 to C4-T5
Artifacts saved under outputs/plots

Project Notes

Input dataset from Capstone 2.
Plotting library choice and saved charts.
Correlation matrix and scatter plots.
Code screenshots and plot artifacts.

Project File Links

Capstone 2 Updated Input CSV: Open Capstone 2 Updated Input CSV
Primary handoff dataset used for Capstone 4 visualization work.
Notebook File: Open Notebook File
Staged Capstone 4 notebook containing the chart-generation commands and observations.
Reference Source CSV: Open Reference Source CSV
Original NSMES source copy kept with the Capstone 4 assets.
Notebook Source: Open Notebook Source
Public notebook source path used when a Colab launch URL is configured.

View Download

Outputs And Results

Key Outputs

Seven PNG artifacts are present under outputs/plots/.
The correlation matrix and the three scatter plots preserve the Week 4 relationship-analysis evidence directly as downloadable artifacts.
The screenshot evidence cards preserve the code used to create the saved figures, so the page shows both implementation and output artifacts.

Key Findings

The categorical plots confirm the heavy concentration of `average` health responses and the larger `other` region population.
The notebook reports meaningful relationships such as `emergency~hospital` and `ovisits~novisits`, while also calling out the mechanically perfect derived-feature correlations.
The saved plot bundle is complete enough to act as the primary grading artifact set for this capstone page.