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Email_Spam_Model.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "939855ce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fba51ea4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df=pd.read_csv(\"spam2.csv\",encoding=\"latin-1\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "07c9eb3d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.head(6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f8c8f1d7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fe0d6616",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.drop(['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b5865574",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5f96657e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df.rename(columns={'v1': 'class', 'v2': 'message'})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4d3cdb2f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0e196722",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['class']=df['class'].map({'ham':0, 'spam':1})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7279b415",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "06d05ab6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.feature_extraction.text import CountVectorizer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dcba8c4a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cv=CountVectorizer()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb354066",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x=df['message']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a64ff973",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y=df['class']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ea3ad8e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df685f7b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ed8a1948",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x=cv.fit_transform(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dca72cdb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4f96137d",
+   "metadata": {},
+   "source": [
+    "1.The Cat\n",
+    "2.The Dog \n",
+    "3.The rat\n",
+    "\n",
+    "   The Cat Dog rat\n",
+    "1.   1   1   0   0\n",
+    "2.    1    0   1  0\n",
+    "3.    1    0   0  1\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "877394e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection  import train_test_split\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "563bd95e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train, x_test, y_train, y_test=train_test_split(x,y, test_size=0.2)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "81b47b81",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1b33ace6",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.naive_bayes import MultinomialNB"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "829d8553",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model=MultinomialNB()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e4e5310d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.fit(x_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "295a5529",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result=model .score(x_train, y_train)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "98b97f5a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result*100\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9fa0997c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "920727b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pickle.dump(model,open(\"spam2.pkl\",\"wb\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5e7e327a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pickle.dump(cv,open(\"vectorizer.pkl\",\"wb\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "512372fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clf=pickle.load(open(\"spam2.pkl\",\"rb\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4fae6f43",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d8a6869c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "msg =\"hi Sandeep Yu are a hero\"\n",
+    "df=[msg]\n",
+    "vect=cv.transform(df).toarray()\n",
+    "result= model.predict(vect)\n",
+    "print(result)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

app.py

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+import pickle
+import streamlit as st
+model=pickle.load(open("spam2.pkl","rb"))
+cv=pickle.load(open("vectorizer.pkl","rb"))
+
+
+def main():
+    st.title("Email Spam Detector Apps")
+    
+
+    main()

spam2.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fd76bf3689fef8561ef859095d99e15ebbf89e9e31d73ed1f701280df896f939
+size 278126

vectorizer.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e9f813b5ed0db70adceb40a68f34082892a7ac408ba19f35328e5128805397b8
+size 105805