sources codes added for third video

selmanbaskaya · Apr 4, 2021 · 5f2b7c3 · 5f2b7c3
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diff --git a/ML-Project/.ipynb_checkpoints/model-checkpoint.ipynb b/ML-Project/.ipynb_checkpoints/model-checkpoint.ipynb
@@ -1088,7 +1088,79 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "#Train Test Split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(data.iloc[:,0:-1], data.iloc[:,-1], test_size=0.2, random_state=0)\n",
+    "X_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Linear Regression\n",
+    "regressor = LinearRegression()\n",
+    "regressor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "regressor.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Linear Regression Predict\n",
+    "y_pred = regressor.predict(X_test)\n",
+    "y_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#backward elimination with statsmodels\n",
+    "X = np.append(arr = np.ones((969,1)).astype(int), values = data, axis=1)\n",
+    "x_list = np.array(data.iloc[:,0:-1], dtype = float)\n",
+    "model = sm.OLS(data.iloc[:,-1], x_list).fit()\n",
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = np.append(arr = np.ones((969,1)).astype(int), values = data, axis=1)\n",
+    "x_list = np.array(data.iloc[:,[0,1,2,3,5,6]], dtype = float)\n",
+    "model = sm.OLS(data.iloc[:,-1], x_list).fit()\n",
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = np.append(arr = np.ones((969,1)).astype(int), values = data, axis=1)\n",
+    "x_list = np.array(data.iloc[:,[0,2,3,5,6]], dtype = float)\n",
+    "model = sm.OLS(data.iloc[:,-1], x_list).fit()\n",
+    "model.summary()"
+   ]
   }
  ],
  "metadata": {

diff --git a/ML-Project/model.ipynb b/ML-Project/model.ipynb
@@ -1088,7 +1088,79 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "#Train Test Split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(data.iloc[:,0:-1], data.iloc[:,-1], test_size=0.2, random_state=0)\n",
+    "X_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Linear Regression\n",
+    "regressor = LinearRegression()\n",
+    "regressor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "regressor.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Linear Regression Predict\n",
+    "y_pred = regressor.predict(X_test)\n",
+    "y_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#backward elimination with statsmodels\n",
+    "X = np.append(arr = np.ones((969,1)).astype(int), values = data, axis=1)\n",
+    "x_list = np.array(data.iloc[:,0:-1], dtype = float)\n",
+    "model = sm.OLS(data.iloc[:,-1], x_list).fit()\n",
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = np.append(arr = np.ones((969,1)).astype(int), values = data, axis=1)\n",
+    "x_list = np.array(data.iloc[:,[0,1,2,3,5,6]], dtype = float)\n",
+    "model = sm.OLS(data.iloc[:,-1], x_list).fit()\n",
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = np.append(arr = np.ones((969,1)).astype(int), values = data, axis=1)\n",
+    "x_list = np.array(data.iloc[:,[0,2,3,5,6]], dtype = float)\n",
+    "model = sm.OLS(data.iloc[:,-1], x_list).fit()\n",
+    "model.summary()"
+   ]
   }
  ],
  "metadata": {

diff --git a/readme.MD b/readme.MD
@@ -7,18 +7,19 @@
 
 ****
 
-- [YouTube Channel](https://www.youtube.com/channel/UCqaZGIkvE8IrgyCdnTAF8Hw)
+- [YouTube Channel](https://www.youtube.com/SelmanBaskaya)
 - [Tanıtım | Kanal İçeriği | Machine Learning & Web Development Projesi #0](https://www.youtube.com/watch?v=BkprXbS6tA4)
 - [Veri Tipleri, Kolon ve Encoding İşlemleri | Machine Learning & Web Development Projesi #1](https://www.youtube.com/watch?v=uba0iHWMsCY)
+- [train_test_split() - Linear Regression - statsmodels | Machine Learning & Web Development Projesi #2](https://www.youtube.com/watch?v=IPCLX1E0mo4)
 
 ****
 
 ## ToDo List
 ### Machine Learning
 - [x] Data labeling
 - [x] Data preparation
-- [ ] Training of the ML Model
-- [ ] Optimization of ML Model
+- [x] Training of the ML Model
+- [x] Optimization of ML Model
 
 ### Web Application
 - [ ] Install Flask