Update README.md

README.md

@@ -15,7 +15,7 @@ license: mit
 To prevent the model from over-fitting on near-identical entries, a robust deduplication pipeline was implemented:
 
 1.  **Vectorization:** Character-level **TF-IDF vectors** were generated from training titles using word-boundary character n-grams (length 3–5).
-2.  **Similarity Analysis:** Near-duplicate titles were identified using a **Cosine Similarity threshold of $\ge 0.85$**.
+2.  **Similarity Analysis:** Near-duplicate titles were identified using a **Cosine Similarity threshold of more than or equal to 0.85**.
 3.  **Clustering:** Records exceeding this threshold were grouped using a **connected-components graph algorithm**.
 4.  **Selection:** Only the first occurrence in file order from each group was retained in the training pool.
 
@@ -26,7 +26,7 @@ To prevent the model from over-fitting on near-identical entries, a robust dedup
 To ensure the test set provides a truly unseen and honest evaluation, the following steps were taken:
 
 * **Shared Feature Space:** The TF-IDF vectorizer was fit on the **combined** set of training and test titles.
-* **Cross-Set Comparison:** Any training record with a **Cosine Similarity $\ge 85\%$** to any record in the test set was permanently removed from the training pool.
+* **Cross-Set Comparison:** Any training record with a **Cosine Similarity threshold of more than or equal to 0.85** to any record in the test set was permanently removed from the training pool.
 * **Test Set Integrity:** The test set itself was deduplicated using **exact title matching only** (no fuzzy matching applied).
 
 > [!IMPORTANT]