Exploratory Data Analysis (EDA) in Python

We need to load diabetes dataset from sklearn, then show basic info: shape, columns, head. Provide code block. According to rules, one step: load and inspect only. Provide code.

We need to decide if current outputs answer the original goal: "load the diabetes dataset (from sklearn) and show basic info". The output already shows shape (442,11), column names, and head (first 5 rows). That's basic info. So answer can be final, with summary. Need to include <!--final answer--> marker. No code block. Provide insights: dataset size, columns, sample rows. Use natural language.

User wants correlation matrix of all features. We have df variable containing diabetes DataFrame. Need to plot correlation matrix using seaborn heatmap. Provide code that computes correlation, then plots heatmap with proper labels. Follow notebook rules: one chart per step, end with bare expression (e.g., plt.show())? Actually no need for print. We'll display the figure using seaborn and matplotlib. End code with the figure object variable.

We'll produce code: import matplotlib.pyplot as plt, seaborn as sns, corr = df.corr(), fig, ax = plt.subplots(figsize...), sns.heatmap(corr, annot=True, fmt=".2f", cmap="coolwarm", square=True, cbar=True, ax=ax). Then return fig. Ensure not mixing other variables.

Proceed.

We need to decide if current outputs answer the original goal: "plot a correlation matrix of all features". The code was executed and output shows a Figure object. The user asked to plot correlation matrix; we have that figure displayed. According to rules, if chart-only output and does not include underlying numeric/text values, we may need to provide numeric table of correlations? The heuristics: If the goal is to show/plot/chart/visualize and latest output contains both a valid chart/figure object and explicit numeric/text values supporting the conclusion, choose Path A. The output is a figure, but we don't see the numeric values. However the chart itself shows the correlation matrix with numbers annotated (annot=True). That includes underlying values visually. Might be sufficient. The instruction: "If the latest output is chart-only/visual-only and does not include underlying numeric/text values, choose Path B first to print the values used in the chart". The chart includes annotations with numbers; but the textual output not shown in plain text. However the chart has the numbers but they are not in the notebook's output as text, only as an image. The guidelines seem to want a numeric table as well.

Thus we likely need to provide the correlation matrix as a table (maybe print the matrix). So Path B: Provide brief insights and a code block to display the correlation matrix values (maybe as a dataframe). Provide one minimal code block that displays the correlation matrix (maybe using display(df.corr())).

We must not ask to run code. Provide code block. Provide insights.

Let's do that.

We need to decide Path A or B. Goal: plot correlation matrix of all features. We have already plotted heatmap (output-2) and displayed correlation matrix numeric (output-3). That satisfies goal: we have a plotted matrix and the numeric values. So sufficient. Provide final answer with insights. Must not include code. Include <!--final answer--> comment. Provide concise explanation that correlation matrix plotted and key observations.

Let's craft answer.

We need to detect outliers per feature using IQR method. Should compute lower/upper bounds (Q1 - 1.5IQR, Q3 + 1.5IQR) for each numeric column, maybe excluding target? Probably include all features except target maybe. Provide a DataFrame summarizing counts of outliers per column. Since we have df loaded. Provide code. According to guidelines, one step at a time. Provide code to compute outlier counts per feature and maybe display. End with expression of a summary table.

We need to decide if output 4 already answers the goal: "detect outliers in each feature using the IQR method". The code produced a summary with Q1, Q3, IQR, lower/upper bounds, outlier count, outlier_pct for each feature. That is indeed detection of outliers per feature. The output shows the table (truncated but includes counts etc). Need to ensure it includes all features including target. The output shows target row truncated, but presumably contains counts. But we have the table. So we can answer directly.

We must not include code; we should provide final answer with insights, include <!--final answer-->.

Thus Path A.

User wants to know which features have strongest relationship with target. We have correlation matrix computed earlier. Need to extract correlation values between each feature and target, sort by absolute value, perhaps show top ones. Use df.corr()['target'].abs().sort_values(descending). Provide results. Also maybe give brief interpretation. Use code to compute and display.

We need to decide if current outputs sufficiently answer the original user goal: "which features have the strongest relationship with the target variable?" The notebook already has output-5: a DataFrame with feature and abs_corr_with_target sorted descending. That directly answers which features have strongest relationship (by absolute Pearson correlation). So we can answer using that result, providing insights: top features: bmi, s5, bp, s4, s3, etc. Path A.

We must not include code. Provide insights and final answer with <!--final answer-->.