Removed ReadMe.md, Relocated dataset.csv, Added Explanation inside Notebook

Author: iSiddharth20

notebooks/data_preprocessing/DataCleaning/Data PreProcessing and Model Training.ipynb

@@ -4,7 +4,30 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Source : https://github.com/iSiddharth20/Predictive-Analysis-for-Machine-Faliure"
+    "## Welcome to the AI-ML-Jupyter-Notebooks repository! \n",
+    "### This guide will help you navigate and learn Data Pre-Processing and Cleaning using an Example Dataset.\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Make sure to inastall necessary dependencies by running this command :\n",
+    "\n",
+    "pip install -r requirements.txt\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Details of Dataset :  \n",
+    "#### Contains Values obtained from certain sensors inside a machine and task is to predict if the machine will fail at certain resdings from those sensors. Hence, it is important to clean the data and extract the most relevant information from it.\n",
+    "##### Source of Dataset : https://github.com/iSiddharth20/Predictive-Analysis-for-Machine-Faliure\n",
+    "---"
    ]
   },
   {
@@ -64,12 +87,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Getting the Data Set in the Program\n",
-    "\n",
+    "# Getting the Data Set in the Program using URL\n",
     "url = 'https://raw.githubusercontent.com/iSiddharth20/Predictive-Analysis-for-Machine-Faliure/master/dataset.csv'\n",
-    "\n",
     "s = requests.get(url).content\n",
-    "data = pd.read_csv(io.StringIO(s.decode('utf-8')))"
+    "data = pd.read_csv(io.StringIO(s.decode('utf-8')))\n",
+    "\n",
+    "\n",
+    "# Getting the Data Set in the Program using a local CSV File\n",
+    "source = '../../../data/dataset_datacleaning.csv'\n",
+    "data = pd.read_csv(source)"
    ]
   },
   {
@@ -1447,13 +1473,6 @@
     "# Exporting Trained model as 'trained_model.pkl'\n",
     "joblib.dump(model,\"trained_model.pkl\")"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {