init

gitattributes

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+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
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+*.model filter=lfs diff=lfs merge=lfs -text
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pass_str.py

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+import pandas as pd
+import numpy as np
+import getpass
+import joblib
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+
+
+
+
+def word(password):
+    character=[]
+    for i in password:
+        character.append(i)
+    return character
+
+
+data = pd.read_csv("data.csv")
+data=data.dropna()
+data["strength"]=data["strength"].map({0:"weak",1:"meduim",2:"strong"})
+
+
+
+def new():
+    
+    
+    
+    x = np.array(data["password"])
+    y = np.array(data["strength"])
+
+    tdif = TfidfVectorizer(tokenizer=word)
+    x = tdif.fit_transform(x)
+    xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size=0.05, random_state=42)
+    x = np.array(data["password"])
+    y = np.array(data["strength"])
+
+    tdif = TfidfVectorizer(tokenizer=word)
+    x = tdif.fit_transform(x)
+    joblib.dump(tdif,"tdif.joblib")
+
+    model = RandomForestClassifier()
+    model.fit(xtrain, ytrain)
+    joblib.dump(model, "passmodel.joblib")
+    
+
+
+
+m=joblib.load("passmodel.joblib")
+td=joblib.load("tdif.joblib")
+user = input("Enter Password: ")
+
+data = td.transform([user])
+output = m.predict(data)
+print(output)

passmodel.joblib

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+size 1065238337

tdif.joblib

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