Create README.md

README.md

@@ -0,0 +1,482 @@
+---
+---
+tags:
+  - regression
+  - classification
+  - clustering
+  - tabular
+  - linkedin
+  - job-postings
+  - sklearn
+  - random-forest
+  - decision-tree
+  - kmeans
+  - shap
+license: mit
+---
+
+# 📊 LinkedIn Job Posting Engagement Analysis
+
+> **Which LinkedIn job posting characteristics predict candidate engagement (views) — and how well can engagement be predicted or classified using only posting-level features?**
+
+**Personal motivation:** As someone in entrepreneurship, understanding which job posting features attract candidates is directly relevant to future hiring decisions.
+
+---
+
+## 📹 Presentation Video
+
+<video src=["https://huggingface.co/datasets/YOUR_USERNAME/YOUR_REPO/resolve/main/presentation.mp4](https://www.loom.com/share/c7d9b89a54234f699204b16a9a313c7d)" controls style="max-width:720px;"></video>
+
+---
+
+## 🚀 Interactive Dashboard
+
+👉 **[Open the LinkedIn Job Engagement Dashboard](https://huggingface.co/spaces/MichaelYitzchak/linkedin_Job_Engagement)**
+
+| Tab | Description |
+|---|---|
+| 🎯 Engagement Predictor | Enter posting details → get predicted views + High/Normal classification in real time |
+| 📊 EDA Dashboard | All 5 EDA findings as interactive charts |
+| ℹ️ About | Feature groups, model details, limitations |
+
+---
+
+## 📋 Dataset at a Glance
+
+| Property | Value |
+|---|---|
+| **Source** | [LinkedIn Job Postings — arshkon/linkedin-job-postings (Kaggle)](https://www.kaggle.com/datasets/arshkon/linkedin-job-postings) |
+| **Original size** | 123,850 rows × 49 columns |
+| **Working sample** | 30,000 rows · `random_state=42` |
+| **After join with companies** | 30,000 rows × 40 columns |
+| **After cleaning** | 29,572 rows × 51 columns (in `df_model`) |
+| **Train / Test split** | 23,657 / 5,915 (80/20, `random_state=42`) |
+| **Regression target** | `log_views = log1p(views)` — log-transformed to handle right skew |
+| **Classification target** | `high_engagement` — top 25% of training views (threshold derived from training set only) |
+
+---
+
+## ⚠️ Scope & Limitations
+
+> LinkedIn's algorithm, sponsored status, and company follower counts drive the **majority of view variance** and are **unobservable** in this dataset. Models use posting-level features only. The practical goal is **ranking postings by predicted engagement**, not exact point prediction. Results show associations, not causal relationships.
+
+---
+
+## 🗂️ Repository Files
+
+| File | Description |
+|---|---|
+| `notebook.ipynb` | Full pipeline: Cleaning → EDA → Feature Engineering → Clustering → Regression → Classification → Bonus |
+| `linkedin_regression_model.pkl` | Winning regression model: Random Forest (Tuned via RandomizedSearchCV) |
+| `linkedin_classification_model.pkl` | Winning classification model: Decision Tree (max_depth=8, class_weight="balanced") |
+| `regression_model_results.csv` | Full regression model comparison table |
+| `classification_model_results.csv` | Full classification model comparison table |
+
+---
+
+## 🧹 Data Cleaning Pipeline
+
+**7 steps from 123,850 raw rows to a clean, leakage-free modelling matrix:**
+
+```
+Step 1 — Reproducible sampling
+        123,850 rows → sample(n=30,000, random_state=42)
+        Joined with companies.csv on company_id (left join, rows preserved)
+        Result: 30,000 rows × 40 columns
+
+Step 2 — Duplicate & missing target removal
+        Removed duplicate rows
+        Dropped rows where views is NaN or negative
+        Result: 29,572 usable rows
+
+Step 3 — Date parsing
+        listed_time, original_listed_time, expiry, closed_time → parsed to datetime
+        Extracted: posting_year, posting_month, posting_dayofweek, posting_weekend
+
+Step 4 — Missing value analysis & column dropping
+        Threshold: >70% missing → drop
+        Dropped: closed_time (99.2%), skills_desc (98.1%), med_salary (95.1%),
+                 remote_allowed (87.9%), applies (81.1%), max_salary/min_salary (76%)
+
+Step 5 — Leakage columns excluded
+        expiry, applies → removed (post-publication outcomes)
+        views → kept as target only, never as a feature
+
+Step 6 — Salary imputation strategy
+        has_salary_info = 1 if salary present, else 0
+        salary_midpoint computed from min/max salary where available
+        Missing salary → imputed inside sklearn Pipeline on training data only
+
+Step 7 — Log transformation of target
+        Raw views: mean=14.9, std=98.8, max=9,949 — heavily right-skewed
+        log_views = log1p(views) — compresses scale, improves regression fit
+        Predictions converted back via expm1() for interpretation
+        Outliers (IQR method): 4,074 (13.8%) — kept, not removed
+```
+
+---
+
+## 🔍 EDA — 5 Research Questions
+
+> **Note on notebook ordering:** Q1=Work type, Q2=Salary, Q3=Description, Q4=Day of week, Q5=Seniority. Presented below in order of business impact.
+
+---
+
+### 💰 Q2 — Salary Transparency vs Views
+
+```
+No salary info   ████████████��░░░░░░░░░░░░  ~12 avg views   (70.1% of postings)
+Has salary info  ████████████████████████░  ~21 avg views   (29.9% of postings)
+                                             +74.3% lift ✓
+```
+
+> Only **8,562 of 29,572 postings (29.9%)** disclose salary. Transparent postings attract **74.3% more views** on average. This is the highest-leverage, lowest-cost recruiter action available.
+
+---
+
+### 📝 Q3 — Description Length vs Views
+
+```
+< 100 words    ██████░░░░░░░░░░░░░░  ~8 avg views   — signals incomplete posting
+100–250 words  █████████░░░░░░░░░░░  ~13 avg views
+250–500 words  ████████████████████  ~24 avg views  PEAK ★ — sweet spot
+500–750 words  ████████████████░░░░  ~18 avg views
+> 1000 words   ███████░░░░░░░░░░░░░  ~10 avg views  — overwhelms candidates
+```
+
+> Non-linear relationship confirmed. Sweet spot: **250–500 words**. This motivated `description_density` — the **#1 feature** in the winning regression model.
+
+---
+
+### 📅 Q4 — Day of Week vs Views
+
+```
+Monday    ████████████████████  39 avg views  ★ best day  (n=1,837)
+Tuesday   █████████████████░░░  25 avg views
+Wednesday ████████████████░░░░  22 avg views
+Thursday  ███████████████░░░░░  18 avg views
+Friday    ███░░░░░░░░░░░░░░░░░   7 avg views  ✗ worst day (n=10,076)
+Saturday  ████████████░░░░░░░░  28 avg views  (weekend — n=2,116 total, noisier)
+Sunday    ████████████░░░░░░░░  28 avg views  (weekend — noisier)
+```
+
+> **Counterintuitive finding:** Weekend postings show higher averages (~28), but the weekend sample is tiny (2,116 obs total) making these estimates unreliable. Monday is the clear best weekday at 39 avg views. The day-of-week signal is modest and should not override content features.
+
+---
+
+### 💼 Q1 — Work Type vs Views
+
+```
+Contract    ████████████████████  29.97 avg views  median: 7.0
+Internship  █████████████████░░░  25.71 avg views  median: 5.0
+Full-time   ████████░░░░░░░░░░░░  13.70 avg views  median: 4.0  ← 80% of volume
+Other       ███████░░░░░░░░░░░░░  11.27 avg views  median: 4.0
+Part-time   ██████░░░░░░░░░░░░░░   9.59 avg views  median: 4.0
+```
+
+> Contract and Internship roles show the highest engagement. However, **Full-time dominates volume** (23,674 of 29,572 postings = 80%). Work type is a useful predictive feature but should not be interpreted as causal.
+
+---
+
+### 🎓 Q5 — Seniority Level vs Views
+
+```
+Entry-level  ████████████████████  18 avg views  n=792
+Senior-level ████████████░░░░░░░░  16 avg views  n=3,577
+Other/Mid    ██████████░░░░░░░░░░  15 avg views  n=25,203
+
+Entry vs Senior: +12.4% more views
+Entry vs Other:  +18.9% more views
+```
+
+> Supply-side effect — more candidates qualify for junior roles, so the pool is larger. `is_entry_role` carries predictive signal because it proxies for **candidate pool size**, not intrinsic desirability.
+
+---
+
+### 🔥 Feature Correlation with log(views+1)
+
+```
+Feature                      Corr    Direction   Note
+─────────────────────────────────────────────────────────────────────
+desc_salary_interaction      +0.18   ↑ views     strongest single predictor
+has_salary_info              +0.14   ↑ views     salary transparency
+salary_log                   +0.12   ↑ views     salary level
+description_density          +0.10   ↑ views     content quality
+description_word_count       +0.08   ↑ views     description length
+is_software_role             +0.08   ↑ views     tech role demand
+is_data_role                 +0.07   ↑ views     data role demand
+is_entry_role                +0.06   ↑ views     larger candidate pool
+posting_weekend              -0.04   ↓ views     small negative signal
+is_senior_role               -0.03   ↓ views     smaller candidate pool
+─────────────────────────────────────────────────────────────────────
+Internal correlations (structural — not data leakage):
+salary_log ↔ salary_midpoint  +0.96  log transform of same variable
+desc_wc ↔ desc_density        +0.55  density uses length in formula
+is_software ↔ is_data         +0.35  often co-occur in job titles
+is_senior ↔ is_entry          -0.28  mutually exclusive by construction
+```
+
+> Most features show **weak linear correlation** — no single feature dominates. This motivated tree-based models (Random Forest, Gradient Boosting) which capture non-linear interactions and feature combinations.
+
+### 🌡️ Correlation Heatmap (feature-to-feature + target)
+
+```
+                          log   desc  has   sal   desc  is_   is_   is_   post  is_
+                          views dens  sal   log   wc    soft  data  entr  wknd  snr
+──────────────────────────────────────────────────────────────────────────────────────
+log_views              │  1.00  0.10  0.14  0.12  0.08  0.08  0.07  0.06 -0.04 -0.03
+description_density    │  0.10  1.00  0.02  0.04  0.55  0.01  0.01 -0.01  0.00  0.00
+has_salary_info        │  0.14  0.02  1.00  0.72  0.03  0.06  0.07 -0.03 -0.01 -0.02
+salary_log             │  0.12  0.04  0.72  1.00  0.04  0.05  0.06 -0.02 -0.01 -0.01
+description_word_count │  0.08  0.55  0.03  0.04  1.00  0.01  0.01 -0.01  0.00  0.00
+is_software_role       │  0.08  0.01  0.06  0.05  0.01  1.00  0.35 -0.08  0.00 -0.05
+is_data_role           │  0.07  0.01  0.07  0.06  0.01  0.35  1.00 -0.06  0.00 -0.04
+is_entry_role          │  0.06 -0.01 -0.03 -0.02 -0.01 -0.08 -0.06  1.00  0.01 -0.28
+posting_weekend        │ -0.04  0.00 -0.01 -0.01  0.00  0.00  0.00  0.01  1.00  0.00
+is_senior_role         │ -0.03  0.00 -0.02 -0.01  0.00 -0.05 -0.04 -0.28  0.00  1.00
+──────────────────────────────────────────────────────────────────────────────────────
+Key structural correlations:
+  salary_log ↔ has_salary_info  +0.72  same underlying signal, different form
+  desc_wc    ↔ desc_density     +0.55  density formula uses word count
+  is_software ↔ is_data         +0.35  frequently co-occur in job titles
+  is_entry   ↔ is_senior        -0.28  mutually exclusive flags
+```
+
+> The heatmap confirms no multicollinearity crisis — the highest inter-feature correlation (salary_log ↔ has_salary_info at 0.72) is a structural relationship between two forms of the same signal, not a data problem. All correlations with log_views are weak, validating the move to non-linear tree-based models.
+
+---
+
+## ⚙️ Feature Engineering — 20 Base + 6 Cluster = 30 Total Features
+
+| Group | Features |
+|---|---|
+| Text length | `title_length`, `title_word_count`, `description_length`, `description_word_count` |
+| Text structure | `description_density` ★, `title_desc_ratio` |
+| Salary | `salary_midpoint`, `salary_range`, `has_salary_info`, `salary_log` |
+| Role keywords | `is_senior_role`, `is_entry_role`, `is_software_role`, `is_data_role`, `is_manager_role`, `is_sales_role`, `is_marketing_role`, `is_remote_text` |
+| Interactions | `desc_salary_interaction` ★, `senior_salary`, `weekend_remote`, `title_desc_word_interaction`, `salary_density_interaction`, `salary_description_interaction`, `title_density_interaction` |
+| Clustering | `cluster_0`, `cluster_1`, `cluster_2`, `cluster_3`, `cluster_4`, `cluster_5` |
+
+**Missing value strategy:**
+- Columns with >70% missing → dropped
+- Salary → `has_salary_info` flag + `salary_midpoint` where available; remaining NaN imputed inside sklearn Pipeline on training data only
+- Remaining numeric → `SimpleImputer(strategy="median")` inside Pipeline
+
+---
+
+## 🔵 Clustering — KMeans k=6
+
+**Features used for clustering (12 total, leakage-checked):**
+`title_word_count`, `description_word_count`, `salary_log`, `description_density`, `has_salary_info`, `is_senior_role`, `is_entry_role`, `is_software_role`, `is_data_role`, `is_manager_role`, `is_sales_role`, `is_marketing_role`
+
+**Methods used to select k:**
+1. Elbow method — inconclusive, no sharp elbow
+2. KMeans silhouette scores on full training matrix
+3. Cluster-size stability table
+4. Interactive K-Means widget (visualization aid — uses sample)
+5. Hierarchical clustering dendrogram (Ward linkage, 300 obs)
+6. Agglomerative clustering comparison (k=2–10)
+
+```
+Silhouette scores by k (full training matrix):
+
+  k=2   ████████░░░░░░░░░░░░  0.198  smallest cluster: 6,830 (28.9%)
+  k=3   █████████░░░░░░░░░░░  0.221  smallest cluster: 2,100 (8.9%)
+  k=4   ████████████████░░░░  0.312  ← strong BUT largest=72% of data
+  k=5   ██████████░░░░░░░░░░  0.250  smallest: 526 (unstable)
+  k=6   ████████████░░░░░░░░  0.290  ← SELECTED ★  smallest: 583 (2.5%)
+  k=7   ████████████░░░░░░░░  0.286  singleton cluster appeared
+  k=8+                               singleton clusters appeared
+
+Why NOT k=10 (highest score): singleton cluster (1 observation)
+Why NOT k=4 (strong score):   largest cluster = 72% — not meaningful separation
+Why k=6: no singletons, stable sizes, interpretable profiles, silhouette 0.290
+```
+
+**Cluster profiles at k=6 (training set n=23,657):**
+
+| Cluster | Label | Size | Share | Key Signal |
+|---|---|---|---|---|
+| 0 | Manager-focused | 4,571 | 19% | `is_manager_role=1.00` |
+| 1 | General / Mixed | 13,055 | 55% | No dominant role signal |
+| 2 | Salary-transparent | 1,940 | 8% | `has_salary_info=1.00` |
+| 3 | Data roles | 1,451 | 6% | `is_data_role=1.00` |
+| 4 | Software roles | 2,057 | 9% | `is_software_role=1.00` |
+| 5 | Entry / low salary | 583 | 2% | Smallest cluster |
+
+**Official final silhouette score: 0.290** (full training matrix)
+
+Cluster labels one-hot encoded as 6 dummy features. Including clusters improved both regression RMSE and classification F1 over models without them.
+
+---
+
+## 📈 Regression — Predicting `log1p(views)`
+
+### Baseline
+
+```
+Mean Baseline (predict training mean for all observations):
+  RMSE_log = 0.8708   R² = -0.0002   ← floor every model must beat
+  MAE_views ≈ 10.64
+
+Baseline Linear Regression (20 features, no clustering):
+  RMSE_log = 0.8425   R² = 0.0639
+```
+
+### Full Model Comparison (after feature engineering + clustering)
+
+| Model | RMSE_log ↓ | R² ↑ | Notes |
+|---|---|---|---|
+| **Random Forest (Tuned) ★** | **0.8347** | **0.0811** | RandomizedSearchCV winner |
+| Random Forest (Controlled) | 0.8349 | 0.0807 | Manual constraints |
+| Gradient Boosting | 0.8370 | 0.0770 | — |
+| Linear Regression + Features | 0.8420 | 0.0640 | — |
+| RidgeCV | 0.8420 | 0.0640 | — |
+| Lasso Regression | 0.8430 | 0.0640 | — |
+| PCA + Linear Regression | 0.8440 | 0.0600 | 15 components, 96.3% variance |
+| Mean Baseline | 0.8708 | -0.0002 | Floor |
+
+**Key lessons:**
+- Unrestricted RF → train R²=0.854, test R²=0.003 (massive overfit). Fixed by `max_depth`, `min_samples_split`, `min_samples_leaf`, `max_features` constraints.
+- 3-fold CV mean RMSE_log: 0.8747 (±0.0125) — stable across folds
+- Outlier robustness test: capping views at 99th pct → RMSE_log 0.8147, R²=0.0812
+
+### Top Feature Importances (RF Tuned)
+
+```
+description_density          ████████████  #1 — content quality proxy
+description_length           ██████████░░  #2 — raw description size
+description_word_count       ████████░░░░  #3 — word count
+title-description interaction████████░░░░  #4 — combined text signal
+is_software_role             ██████░░░░░░  #5 — tech role demand
+is_data_role                 █████░░░░░░░  #6 — data role demand
+salary_log / has_salary_info ████░░░░░░░░  #7+ — salary signals
+```
+
+> `desc_salary_interaction` ranks #2 in SHAP analysis but further down in Gini importance — both agree on description quality and salary as top drivers.
+
+### Why R² = 0.081 Is Acceptable
+
+```
+R² = 0.081 → model explains ~8% of variance in log(views+1)
+
+✓ Beats mean baseline (R²≈0) — real posting-level signal captured
+✓ Social engagement inherently noisy — platform factors dominate
+✓ 92% of variance from unobservable sources (algorithm, followers, ads)
+✓ Practical use = ranking postings, not forecasting exact counts
+```
+
+---
+
+## 🟠 Classification — High Engagement vs. Normal
+
+```
+Target: high_engagement = 1 if views ≥ 75th percentile of TRAINING views
+Class balance: ~75% Normal (Class 0) / ~25% High Engagement (Class 1)
+Feature matrix: X_clf uses 24 features (see notebook cell 207)
+Metric: F1-score for Class 1 (accuracy misleading with 75/25 imbalance)
+```
+
+### Model Comparison
+
+| Model | F1 (Class 1) | Recall (Class 1) | Notes |
+|---|---|---|---|
+| **Decision Tree ★** | **HIGHEST** | **HIGHEST** | max_depth=8, class_weight="balanced" |
+| Logistic Regression | near-best | high | Close to DT in F1 |
+| Random Forest | moderate | lower | Lowest FP count |
+| Dummy Baseline | 0.00 | 0.00 | Always predicts Class 0 |
+
+**5-fold CV F1: 0.4424 ± 0.0152** — stable, no lucky split
+
+### Error Cost Analysis
+
+```
+FN (missed high-engagement) = most costly error
+  → Company fails to prioritize, promote, or learn from a strong posting
+
+FP (false alarm) = also costly
+  → Recruiter wastes time and budget on a posting that won't perform
+```
+
+Decision Tree minimises FN (catches most high-engagement postings) but produces more FP.
+Random Forest minimises FP (fewest false alarms) but misses more high-engagement postings.
+
+---
+
+## 💡 Business Insights
+
+1. **Salary transparency is the single highest-leverage action** — 74.3% more views for free. Fewer than 30% of postings disclose salary today.
+2. **Description structure matters** — `description_density` was the #1 feature in both models. Sweet spot: 250–500 words.
+3. **Tech roles attract disproportionate engagement** — `is_software_role` and `is_data_role` carry real signal beyond salary.
+4. **Work type is associated with engagement** — contract roles lead, but full-time dominates volume (80%).
+5. **Platform factors dominate** — R²≈0.08 is expected and acceptable. Model value is in **ranking** postings, not exact prediction.
+
+---
+
+## 🎁 Bonus Work
+
+### 🧠 SHAP Explainability
+
+```
+SHAP mean |value| — RF Tuned regression (test observations)
+
+description_density      ████████████  strongest positive impact ↑
+desc_salary_interaction  ██████████░░  salary × description synergy ↑
+salary_log               ████████░░░░  salary level ↑
+has_salary_info          ██████░░░░░░  disclosed → more views ↑
+posting_weekend          ██░░░░░░░░░░  weekend → fewer views ↓
+```
+
+`desc_salary_interaction` ranks #2 in SHAP but lower in Gini — confirms it captures genuine non-linear interaction that neither feature achieves alone.
+
+### 📊 Feature Importance: Regression vs Classification
+
+```
+                        Regression RF    Classification DT
+description_density     #1               #2
+desc_salary_interaction #2 (SHAP)        varies
+salary_log              #7+              varies
+is_entry_role           lower            rises in classification
+is_data_role            #6               varies
+──────────────────────────────────────────────────────────
+Agreement:  description quality + salary dominate both models
+Divergence: seniority/role flags matter more for threshold-crossing
+            (classification) than for predicting exact counts (regression)
+```
+
+### 🔬 Additional Extras
+
+- **Interactive K-Means Widget** — explore different k values visually (notebook cell 4.11)
+- **Hierarchical Clustering Dendrogram** — Ward linkage, 300 obs sample (cell 4.12)
+- **Agglomerative Clustering Diagnostic** — k=2–10 comparison (cell 4.13)
+- **Outlier Robustness Test** — views capped at 99th percentile: RMSE_log 0.8147 vs 0.8347 uncapped
+- **3-fold CV for regression** — mean RMSE_log 0.8747 ± 0.0125
+
+---
+
+## 🛠️ How to Use the Models
+
+```python
+import pickle, numpy as np
+
+with open("linkedin_regression_model.pkl", "rb") as f:
+    reg_model = pickle.load(f)
+with open("linkedin_classification_model.pkl", "rb") as f:
+    clf_model = pickle.load(f)
+
+# Regression — predict log(views+1), convert back to raw view estimate
+log_views_pred = reg_model.predict(X_test_fe)
+views_pred = np.expm1(log_views_pred)
+
+# Classification — predict high-engagement label (0 = Normal, 1 = High)
+label = clf_model.predict(X_clf)
+```
+
+> Regression model expects **30-column** `X_test_fe` (including cluster dummies).
+> Classification model expects **24-column** `X_clf` (see notebook cell 207).
+> Run the full pipeline in the notebook to produce compatible feature matrices.
+
+---
+
+*Assignment 2 — Classification, Regression, Clustering, Evaluation | LinkedIn Job Postings · arshkon/linkedin-job-postings (Kaggle)*
+