Implementing BoW.ipynb

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   "source": [
    "# Machine Learning Project : Implementing Bag of Words(BoW) without using sklearn Library"
   ]
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   "source": [
    "-------------------------------------------------"
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   "source": [
    "## Step 1: Convert all strings into thier lower case form"
   ]
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   "source": [
    "# the documents\n",
    "documents = [\"Hello\", \"How are you!\",\n",
    "            \"Wine money, win from home.\",\n",
    "            \"Call me now.\", \"Hello, Call hello you tomorrow?\"]"
   ]
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   "source": [
    "lower_case_documents = [document.lower() for document in documents]"
   ]
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     "text": [
      "['hello', 'how are you!', 'wine money, win from home.', 'call me now.', 'hello, call hello you tomorrow?']\n"
     ]
    }
   ],
   "source": [
    "print(lower_case_documents)"
   ]
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   "source": [
    "----"
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   "source": [
    "## Step 2: Remove all Punctuation\n",
    "\n",
    "**Instructions:** Remove all punctuation from the strings in the document set. Save the strings into a list called `punctuation_documents`. "
   ]
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   "source": [
    "'''\n",
    "Solution\n",
    "'''\n",
    "import string\n",
    "\n",
    "punctuation_documents = []\n",
    "\n",
    "for i in lower_case_documents:\n",
    "    \n",
    "    punctuation_documents.append(i.translate(str.maketrans('','', string.punctuation)))\n",
    "    \n",
    "    "
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   "outputs": [
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     "text": [
      "['hello', 'how are you', 'wine money win from home', 'call me now', 'hello call hello you tomorrow']\n"
     ]
    }
   ],
   "source": [
    "print(punctuation_documents)"
   ]
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   "source": [
    "-----"
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   "source": [
    "## Step 3: Tokenizing\n",
    "**Instructions:** Tokenize the strings stored in `punctuation_documents` using the `split()` method. Store the final document set in a list called `preprocessed_documents`."
   ]
  },
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   "cell_type": "code",
   "execution_count": 11,
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   "outputs": [],
   "source": [
    "'''\n",
    "solution\n",
    "'''\n",
    "preprocessed_documents = [i.split(' ') for i in punctuation_documents]\n"
   ]
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   "outputs": [
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     "text": [
      "[['hello'], ['how', 'are', 'you'], ['wine', 'money', 'win', 'from', 'home'], ['call', 'me', 'now'], ['hello', 'call', 'hello', 'you', 'tomorrow']]\n"
     ]
    }
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   "source": [
    "print(preprocessed_documents)"
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   "source": [
    "---"
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    "## Step 4 and the last step: Count frequencies\n",
    "**Instructions:** Using the `Counter()` method and `preprocessed_documents` as the input, create a dictionary with the keys being each word in each document and the corresponding values being the frequency of occurrence of that word. Save each Counter dictionary as an item in a list called `frequency_list`."
   ]
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   "cell_type": "code",
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   "id": "591bbd89-76c0-43af-99b5-d43f582b7fab",
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   "source": [
    "'''\n",
    "Solution\n",
    "'''\n",
    "from collections import Counter\n",
    "import pprint\n",
    "\n",
    "frequency_list = []\n",
    "\n",
    "for i in preprocessed_documents:\n",
    "    frequency_count = Counter(i)\n",
    "    frequency_list.append(frequency_count)\n"
   ]
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     "text": [
      "[Counter({'hello': 1}), Counter({'how': 1, 'are': 1, 'you': 1}), Counter({'wine': 1, 'money': 1, 'win': 1, 'from': 1, 'home': 1}), Counter({'call': 1, 'me': 1, 'now': 1}), Counter({'hello': 2, 'call': 1, 'you': 1, 'tomorrow': 1})]\n"
     ]
    }
   ],
   "source": [
    "print(frequency_list)"
   ]
  }
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