diff --git a/notebooks/notebook_one_day_data.ipynb b/notebooks/notebook_one_day_data.ipynb
index 1eb90fb..2d7199e 100644
--- a/notebooks/notebook_one_day_data.ipynb
+++ b/notebooks/notebook_one_day_data.ipynb
@@ -193,7 +193,7 @@
     "### Viser temperaturen\n",
     "Regner ut gjennomsnittst-temperatur ved hjelp av innebygde funksjoner. Finner også høyeste og laveste målte temperatur.\n",
     "\n",
-    "Plotter temperaturen ved hjelp av matplotlib."
+    "Plotter gjennomsnittstemperaturen og temperaturen per time for dagen valgt, ved hjelp av matplotlib."
    ]
   },
   {
@@ -220,36 +220,43 @@
     "print(\"Highest temperature:\", max_temp)\n",
     "print(\"Lowest temperature:\", min_temp)\n",
     "\n",
-    "# Set the x_axis to the index, which means the time\n",
+    "\n",
+    "# Set the x_axis to the index, which represents the time\n",
     "x_axis = df.index\n",
     "\n",
     "# Choose the width and height of the plot\n",
     "plt.figure(figsize=(12, 6))\n",
     "\n",
-    "# Plotting temperatur\n",
-    "plt.plot(x_axis, temp, color='tab:red', label='Temperatur')\n",
+    "# Scatter plot for each temperature reading\n",
+    "plt.scatter(x_axis, temp, color='tab:green', label='Temperaturmålinger', alpha=0.6)\n",
     "\n",
-    "# Get the current axsis, and store it as ax\n",
+    "# Add a horizontal line for the mean temperature\n",
+    "plt.axhline(y=temp_mean, color='green', linestyle='--', label=f'Gj.snitt {temp_mean}°C')\n",
+    "\n",
+    "# Get the current axis and store it as ax\n",
     "ax = plt.gca()\n",
     "\n",
     "# Customize the x-axis to show ticks for each hour\n",
     "ax.xaxis.set_major_locator(mdates.HourLocator(interval=1))  # Tick marks for every hour\n",
-    "ax.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))  # Format as \"Day Month Hour:Minute\"\n",
+    "ax.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))  # Format as \"Hour:Minute\"\n",
+    "\n",
+    "# Rotate x-axis labels for better readability\n",
+    "plt.xticks(rotation=45)\n",
     "\n",
     "# Adjust layout\n",
     "plt.tight_layout()\n",
     "\n",
-    "# Add title for the plot, with city_name and start to end date\n",
+    "# Add title for the plot\n",
     "plt.title(f'Temperatur {city_name}, ({date})')\n",
     "\n",
-    "# Shows a grid\n",
+    "# Show grid\n",
     "plt.grid()\n",
     "\n",
-    "# Show the label-description\n",
-    "plt.legend(loc = 'upper right')\n",
+    "# Show legend\n",
+    "plt.legend(loc='upper right')\n",
     "\n",
     "# Show the plot\n",
-    "plt.show()"
+    "plt.show()\n"
    ]
   },
   {
@@ -563,6 +570,131 @@
     "# Show the plot\n",
     "plt.show()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lineær regresjons og prediktiv analyse\n",
+    "\n",
+    "lager en lineær regresjon og bruker den til å finne temperaturer vi kan forvente å få samme tid, neste år.\n",
+    "Bruker scikit-learn biblioteket, sklearn.linear_model, LinearRegression for å få til dette"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "#Hours of the day (0 to 23) and temperatures\n",
+    "data = {\n",
+    "    'Hour': np.arange(24),  # 24 hours of the day\n",
+    "    'Temperature': temp # get the temperatures\n",
+    "}\n",
+    "\n",
+    "# Create a DataFrame\n",
+    "df = pd.DataFrame(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### klargjøre data til regresjonsmodellen"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Reshape the data (features should be 2D for sklearn)\n",
+    "X = df['Hour'].values.reshape(-1, 1)  # Hours of the day (x-axis)\n",
+    "y = df['Temperature'].values  # Temperatures (y-axis)\n",
+    "\n",
+    "# Create and train the regression model\n",
+    "model = LinearRegression()\n",
+    "model.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### trene modellen og predikerte fremtidig data\n",
+    "\n",
+    "trene modellen og lager en graf som viser den originale dataen fra årets dag brukeren har skrevet inn og regresjons modellen\n",
+    "\n",
+    "bruker den trente modellen til å lage predikasjoner for temperaturen samme dag, neste år"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib.dates import DateFormatter\n",
+    "\n",
+    "y = np.array(df['Temperature'].values)\n",
+    "\n",
+    "x_axis = df.index\n",
+    "\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "\n",
+    "# Plot the original data\n",
+    "plt.scatter(x_axis, y, color='green', label='Original data', alpha=0.6)\n",
+    "\n",
+    "predicted_temperatures = model.predict(X)\n",
+    "\n",
+    "# Plot the regression line\n",
+    "plt.plot(x_axis, predicted_temperatures, color='red', label='Prediktiv temperatur')\n",
+    "\n",
+    "#title for the axis\n",
+    "plt.xlabel('Datetime')\n",
+    "plt.ylabel('Temperature (°C)')\n",
+    "\n",
+    "#ax = plt.gca()\n",
+    "\n",
+    "# Customize the x-axis to show ticks for each hour\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.gca().xaxis.set_major_formatter(DateFormatter('%H:%M'))\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "#Title for the plot\n",
+    "plt.title(f'Temperature {city_name}')\n",
+    "\n",
+    "plt.grid(axis = 'x')\n",
+    "\n",
+    "plt.legend(loc='upper right')\n",
+    "\n",
+    "plt.show()\n",
+    "\n",
+    "# Display the predicted temperatures\n",
+    "print(f'predicted temperatures: {predicted_temperatures}')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -581,7 +713,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.5"
+   "version": "3.9.6"
   }
  },
  "nbformat": 4,