diff --git a/machine_learning/feature_extraction/tfidf.py b/machine_learning/feature_extraction/tfidf.py
new file mode 100644
index 000000000000..0d21509a7b1e
--- /dev/null
+++ b/machine_learning/feature_extraction/tfidf.py
@@ -0,0 +1,97 @@
+import numpy as np
+import re
+# to seprate words and normlize it
+
+
+def decompose(text):
+    text = text.lower()
+    text = re.sub(r"[^a-z0-9\s]", "", text)
+    text = re.sub(r"\s+", " ", text)
+
+    return text.split()
+
+
+# creating tfidf class
+class TfIdfVectorizer:
+    def __init__(self):
+        self.vocab = None
+        self.idf = None
+
+    # these method to compute the tf for each word in given data
+    def compute_tf(self, data):
+        tf = []
+        doc_words = []
+
+        for document in data:
+            words = decompose(document)
+
+            freq = {}  # these dictionerie have for each unique words it number of apprition in one sentence
+
+            for word in words:
+                freq[word] = freq.get(word, 0) + 1
+
+                if word not in doc_words:
+                    doc_words.append(word)
+
+            # calculating tf
+
+            for word in freq:
+                freq[word] /= len(words)
+
+            tf.append(freq)
+
+        # computing idf
+        idf = {}
+
+        n = len(data)
+
+        for word in doc_words:
+            df = sum(1 for doc in tf if word in doc)
+            idf[word] = np.log((n + 1) / (1 + df)) + 1
+
+        self.idf = idf
+        tfidf = []
+
+        self.idf = idf
+
+        # computing tfidf for each word
+
+        for doc_tf in tf:
+            vector = [doc_tf.get(word, 0) * idf[word] for word in doc_words]
+            tfidf.append(vector)
+
+        self.vocab = doc_words
+
+        return np.array(tfidf, dtype=float)
+
+    def encode(self, data):
+        if self.vocab is None or self.idf is None:
+            raise ValueError("You should fit the model first")
+
+        tfidf_matrix = []
+        for doc in data:
+            words = decompose(doc)
+            freq = {}
+
+            # Count term frequencies for words that exist in the vocabulary
+            for word in words:
+                if word in self.vocab:
+                    freq[word] = freq.get(word, 0) + 1
+
+            # Normalize TF by document length
+            for word in freq:
+                freq[word] /= len(words)
+
+            # Align vector according to vocab and multiply by IDF
+            vector = [freq.get(word, 0) * self.idf[word] for word in self.vocab]
+            tfidf_matrix.append(vector)
+
+        return np.array(tfidf_matrix, dtype=float)
+
+
+if __name__ == "__main__":
+    documents = ["the cat sat on the mat", "the dog chased the cat"]
+    vectorizer = TfIdfVectorizer()
+    tfidf_matrix = vectorizer.compute_tf(documents)
+    print("Vocabulary:", vectorizer.vocab)
+    print("TF-IDF Matrix:\n", tfidf_matrix)